Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings’ operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples’ feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset. Primary mine fans are essential pieces of equipment in mine ventilation systems, and they are key pieces of mechanical equipment that guarantee safe coal production ,2. A priA primary mine fan usually employs an axial-flow ventilation fan and a high-voltage, explosion-proof, and three-phase asynchronous motor as the matching motor. The fan is directly connected to the motor, as shown in With the increasing growth of computer technology and sensor and signal processing technologies, various studies have been performed on the FD of rolling bearings. Sun et al. presented a bearing FD approach using empirical modal decomposition (EMD) and improved Chebyshev distance . Du et aWith the rapid development of industrial technology, the volume of monitoring data for key components, such as rolling bearings and gearboxes, has increased substantially. Because of the small parameter space, traditional shallow machine learning methods are prone to falling into local extreme points during training and cannot better process a large number of monitoring signals, and the shallow model has high requirements for the input data. The diagnostic performance of the model is highly reliant on signal processing and feature extraction techniques. This leads to difficult training and poor generalization of fault diagnosis models . Deep leHowever, most of the above research relied on the failure test bench in a laboratory environment, which can be operated via human modulation to generate a large number of fault data. A primary mine fan is normally required to work for a long time at coal mine industrial sites. Since the working environment is particular and dangerous, when a fan fails, it is stopped for maintenance immediately, making it challenging to collect fault operation data of the fan under actual working conditions. Moreover, most coal mining enterprises adopt a preventive periodic maintenance program to maintain and manage their primary mine fan, causing difficulties in the collection of fan fault operation data. For these reasons, the accumulation period of the primary mine fan’s rolling bearing fault data is long and time-consuming, and the attainable fault data are few in number and incomplete in type. Accordingly, the monitoring process results in a considerable number of normal data and very few fault data ,19. ThusIn order to promote the diagnostic model’s efficiency under the sample imbalance condition, it is necessary to change the data distribution and alleviate the sample imbalance. The most commonly used approaches are divided into two categories. One category is to improve the FD algorithm to enhance the classification precision of the minority classes. This is generally carried out by changing the loss function of the algorithm to devote suitable weights to the minority class samples. However, such methods may result in models that are overly dependent on loss function design. It may ignore other important features and information, which causes the model to perform poorly in the face of unknown data or changes in data distribution. Moreover, when assigning weights to minority samples, the weight settings need to be trial-and-error and optimized through manual experience. Finding the optimal weight setting requires considerable labor costs and extended experiment time. And with the change in working conditions, such methods need to be designed and adjusted according to the specific data and problems for the loss function and weight settings when used for different datasets, which increases the complexity of practical applications. Thus, such methods suffer from poor robustness, data dependence, and difficulties in setting weights when solving the sample imbalance problem. This leads to these methods providing limited diagnostic performance improvement, and it can be difficult to obtain the optimal weights, which does not completely solve the impact caused by the sample imbalance problem. The other category is to reduce the imbalance at the data level, mainly through sample generation methods to generate more minority class samples to achieve data balance and effectively alleviate the sample imbalance problem. SMOTE, ADASYN, RSMOTE, and other algorithms ,21,22 arWang et al. integrated the variational self-encoder theory with generative adversarial networks and designed an FD method using improved generative adversarial networks . Zhao etAlthough all the above studies have attained some results, some deficiencies still exist. First, these studies employ a modified sample generation method based on GANs, which improves the sample generation ability. However, an optimal evaluation index is required to determine whether the loss function converges. Moreover, training instability and gradient disappearance problems still exist . The expThe denoising diffusion probabilistic model (DDPM)  is a fleDuring training, the generator and discriminator of the GAN are trained using an adversarial loss function with the goal of minimizing the difference between the generated and real samples. Unlike traditional optimization problems, GAN does not have a clear convergence criterion during adversarial training. It usually indirectly assesses whether the loss function converges by observing the quality of the generated samples, the accuracy of the discriminator, and the value of the loss function. Thus, the training process of GAN is unstable and may have problems, such as pattern crashes and nonconvergence of training. Compared with GAN, DDPM is trained using a specific loss function (log-likelihood function) to fit the probability density of the data without the need for adversarial training. This makes the training of DDPM more stable and easier to control as well as making the training process smoother and the possibility of training instability and gradient disappearance problems relatively small. DDPM can also use the log-likelihood value, loss function value, sample reconstruction error, and other evaluation indicators to determine the convergence of the loss function and optimize and adjust the model to improve the performance and effect of DDPM generation. Therefore, DDPM is more suitable for the generation of high-dimensional data and multimodal data.Meanwhile, many researchers have shown that converting vibration signals into images is an effective FD method ,37,38. CTherefore, the current study presents an intelligent FD approach that is suitable for rolling bearings of a primary mine fan under sample imbalance conditions. First, the method converts the RB vibration signal into a distinctive SDP image via the SDP technique. Then, the DDPM method generates image samples with similar characteristics as the real minority image samples to expand the imbalanced dataset in order to achieve sample balance. Finally, a CNN is trained as an FD model to identify the rolling bearing condition. In order to assess the effectiveness of the presented approach, experimental validation was conducted via a conventional rolling bearing dataset and primary mine fan rolling bearing monitoring data under actual working situations.(1)To illustrate the original vibration signal’s fault characteristics in a strong noise environment and improve the FD performance, the SDP approach converts the 1D vibration signal into a simple and intuitive 2D SDP image with apparent features.(2)The DDPM method generates image samples with characteristics similar to the minority class fault image samples, which complements the minority class fault samples in the dataset and effectively reduces the imbalance.(3)Combining the powerful sample generation capability of DDPM with the advantages of CNN in image processing, the current work introduces an FD approach for the rolling bearings of primary mine fans. The illustrated approach is able to resolve the problem of insufficient fault diagnosis accuracy caused by the sample imbalance of rolling bearing faults in primary mine fans under actual working conditions. Moreover, the approach is robust and can attain satisfactory diagnostic efficiency on the conventional rolling bearing dataset and the primary mine fan rolling bearing monitoring data with substantial noise interference under actual working conditions.The essential novelties of the present work are as follows:The remainder of the current paper is arranged as follows: This section introduces the fundamental theory of SDP, DDPM, and CNN methods used in this paper.A symmetrized dot pattern (SDP) can describe the time-series signals’ amplitude and frequency variations and can be applied to visualize speech signals ,40. The The vibration signal is a 1D time-series signal. When the vibration signal is transformed into an image using the SDP method, its amplitude and frequency variations can be reflected by the shape characteristics, geometric center, curvature, concentration area of points, and other elements of the SDP image, which can better describe the change features of the vibration signal. As shown in m/n, m = 1, …, n, where n is the number of symmetric mirror planes, typically 6); and In With the continuous development of depth generation model research, the diffusion model has been extensively utilized in image generation. The concept of the diffusion model initially originated in 2015 . After cAs shown in In the forward-noise addition process, Gaussian noise is continuously added to the original clear data In the reverse denoising process, it is necessary to derive the conditional probability ed study , if q can be simplified using the variational method to describe the model’s training objective with the following objective function at any (1)Obtain the input (2)Acquire a sample of the noise (3)Minimize Accordingly, the training procedure of DDPM can be described as follows:A convolutional neural network (CNN) is a typical feed-forward neural network with significant advantages in 2D image processing. A CNN is based on the extraction of the features of the input data via the construction of multiple filters layer by layer. As shown in In CNNs, the convolutional layer operates by convolving multiple local filters with the original input data through a convolutional kernel to extract local features and output a feature map. The number of output feature maps is the depth of the convolutional layer. After completing the convolutional operation, the activation function is generally utilized to achieve the nonlinear transformation of the output of the convolutional layer to accelerate the convergence of a CNN.The pooling layer usually utilizes a specific statistical feature of the adjacent output at a location to replace the network’s output at that location, which is essentially the scaled mapping of the previous layer’s data graph. The pooling layer is able to efficiently alleviate the data dimension and network parameters, accelerate the CNN calculations, and avoid network overfitting.The fully connected layer constructs a feature vector by sequentially expanding all the extracted feature maps. Its primary function is to integrate the information from the previous layers.A CNN provides some advantages, such as sparse connectivity and weight sharing. Sparse connectivity is able to alleviate the number of training parameters, and weight sharing can efficiently prevent the algorithm’s overfitting. Meanwhile, the batch normalization (BN) layer and dropout technique are usually utilized in CNNs. Of the two, the BN layer performs the normalization operation on each batch of input data, which overcomes any variation in the data distribution in the middle layer, solves the unstable training phenomenon in the deep layer network, and increases the CNN’s convergence rate. The dropout technique reduces the overfitting phenomenon of CNN models by setting parameters to deactivate the neurons in the fully connected layer with a certain probability.The CNN’s training procedure comprises forward and backward propagation. Forward propagation aims to input the sample data into the CNN and obtain the network output. Backpropagation computes the error between the network output and the actual value from the loss function and propagates the error backward. The whole convolutional neural network is trained to minimize the loss function.The current paper proposes an intelligent FD approach for the rolling bearings of primary mine fans under sample imbalance conditions based on the SDP technique, denoising diffusion probabilistic models (DDPMs), and convolutional neural networks (CNNs). (1)The vibration signals are acquired from rolling bearings via sensors and data acquisition instruments.(2)The overlapping sampling approach is employed to preprocess the vibration signal and divide it into several fixed-length samples. The 1D vibration signal is then converted into a 2D SDP image using the SDP approach and taken as the input sample for the FD model. The normal and fault-state samples are the majority and minority class samples, respectively.(3)The input samples are categorized into imbalanced training datasets and balanced test datasets.(4)Training samples of each minority class (fault state) are input into the DDPM individually to generate more samples of generated images with similar feature distributions as the real images.(5)The generated minority class (fault state) samples are added to the unbalanced training dataset to the sample expansion of the minority class samples to balance the training dataset sample. A balanced training dataset containing the generated and original images is then utilized as the input for training the CNN-based FD model so that the model can identify various fault-state features of the bearing.(6)The trained FD model is evaluated using the test dataset, and the efficiency of the presented approach is assessed for the final FD accuracy.The presented FD approach combines the sample generation ability of DDPM with the feature extraction capability of CNN to effectively solve the sample imbalance and insufficient fault diagnosis performance problems induced by insufficient fault samples of the rolling bearings in a primary mine fan. The diagnostic process of the presented approach can be described in the subsequent stages:In this paper, the core process of the proposed rolling bearing fault diagnosis method in primary mine fans includes three steps, namely SDP image conversion, DDPM image generation, and CNN fault diagnosis model training. The method makes full use of the advantages of SDP technology and DDPM, combined with the feature extraction capability of CNN to solve the problems of difficult-to-train fault diagnosis models, insufficient fault diagnosis accuracy, and easy overfitting or misdiagnosis due to sample imbalance. And it is capable of accurately detecting rolling bearing faults in primary mine fans under sample imbalance conditions. Of these, SDP images are able to highlight the fault characteristics of the bearing vibration signals so that they can be easily recognized and extracted, which helps to improve the fault diagnosis performance. The DDPM is used to generate image samples with similar characteristics to the minority category of fault image samples, which effectively expands the imbalance dataset and reduces the degree of data imbalance. In turn, the fault diagnosis model is better able to learn the features of the minority class fault samples and improve the accuracy of fault detection. Finally, the advantages of CNN in image processing and feature extraction tasks are fully utilized to identify and classify the faults of the rolling bearings in primary mine fans. The method proposed in this paper provides a novel and effective solution for the intelligent fault diagnosis of rolling bearings of primary mine fans. Moreover, the method is robust and can provide a reference for other similar problems in engineering practice.The presented approach aims to resolve the sample imbalance issue in the fault diagnosis of the primary mine fan rolling bearings. In order to assess the efficiency of the developed approach, the current paper employs the conventional rolling bearing fault dataset collected in the laboratory and the rolling bearing dataset of a primary mine fan in actual working conditions for experimental validation. All experiments were performed on a computer with the operating system Ubuntu 18.04LTS, CPU Intel(R) Xeon(R) 3204, and GPU Nvidia GeForce GTX 3080. Vibration signal processing and SDP image conversion were performed using MATLAB 2017a. The programming structure for the DL method was PyTorch, and the programming language was Python.In order to evaluate the efficiency of the developed approach, the experiments in the current section employed the QPZZ-II rotating machinery failure test bench dataset for experimental validation. The rolling bearing’s simulated failure part was utilized in the experiment, as presented in The motor’s rotation speed was 1500 r/min, and the load was 0 HP. The wire-cutting technique was utilized to set up a single point of failure on the inner ring, outer ring, and rolling element of the tested bearing. The test bench included an INV9824 accelerometer and an INV3062C collector to acquire bearing vibration signals, and the bearing vibration data’s sampling frequency was 10.24 KHz. First, the normal bearing’s vibration acceleration signal was acquired, and then three kinds of fault rolling bearings  were mounted to the test bench for fault signal collection.After preprocessing the collected vibration data, the overlapping sampling technique was utilized to divide the four operating states’ vibration signals into samples, each containing 2048 sampling points. To perform sample segmentation, the first step was to set the size of the sampling window to 2048. The size of the sampling window determined the length of each sample intercept from the vibration signal. Then, the sampling step size was set to 300, which determined how far the sampling window moved through the vibration signal, allowing it to intercept the next sample. A series of overlapping sets of vibration samples were obtained using the overlapping sampling method. Finally, the vibration samples with the same window length were converted to SDP images and resized to 64 × 64 using the conversion Equations (1)–(3) in g and L can promote the image quality and strengthen the distinctions between signals, allowing more apparent differentiation among various vibration signals [A and B describe the images’ two-dimensional gray matrices. The smaller the correlation coefficient R of different images, the greater the difference between images. In order to better choose the optimum values of As the quality of SDP images directly depends on the parameters  signals ,44. TherThe values of After selecting the optimal parameters, the SDP approach was utilized to transform all vibration signals into two-dimensional SDP images. The time-domain graphs of the four operating state samples of the rolling bearing and the converted SDP images are shown in In order to conduct research on rolling bearing fault diagnosis under sample imbalance conditions, a sample set for training and testing the diagnostic model based on SDP images was constructed. Therefore, 1500 samples were randomly chosen from the normal samples as training samples, and 200 samples were randomly chosen as the test samples. The other three types of faulty samples were randomly chosen from 1000 samples as training samples and 200 samples as test samples. As shown in The normal-state images in the dataset were chosen as majority class samples, and the other three fault-state images were chosen as minority class samples. After obtaining the SDP images of different operating states of rolling bearings, three minority sample images of each class were utilized as the input for training the DDPM and supplementing the imbalance sample set using the DDPM-generated images.In order to validate the efficiency of the DDPM approach, the quality of the produced samples should be evaluated, and their impact on FD under sample imbalance conditions should be studied. The DDPM was modeled using the U-Net, a U-shaped network framework comprising an encoder, a decoder, and cross-layer connections  between the encoder and decoder. The U-Net model first achieves downsampling operations on the input through the encoder and then accomplishes upsampling operations using the decoder, with cross-layer connections employed to splice features between the encoder and decoder. The specific model parameter settings can be found in . In DDPMAs presented in Three minority class images were separately adopted as the input, and the trained DDPM was employed for image sample generation. These were compared with the samples generated via other common generation methods, such as VAE, GAN, and WGAN-GP, as presented in In order to further describe the advantages of the DDPM approach for generating images, the t-SNE method  was usedAs presented in In summary, DDPM can generate high-quality image samples. Compared with other generation approaches, the DDPM-produced samples have a higher similarity to the real samples and are better at enhancing and complementing the unbalanced sample set.The efficiency of deep neural network-based FD approaches depends on the number of training samples. Under the sample imbalance condition, the sample generation method can effectively solve the diagnostic model’s insufficient precision due to the lack of a few classes of training samples. However, the produced samples are derived from the real ones and can only complement, but not completely replace, the real samples for a few classes of real samples.In this study, we conducted different experiments on the imbalance rate of a few classes of samples to assess the influence of the complementary number of produced samples on the fault diagnosis accuracy. According to the multiple imbalance rate settings, in the experiments, a few classes (fault states) of samples were first generated using the DDPM method, and then data augmentation was performed on the imbalance sample set, i.e., the produced samples were used to supplement the real samples and finally obtain a mixed sample set, as described in In all the mixed sample sets, data augmentation was performed only in the training set, and the test set comprised only the original real samples to ensure consistent results. Moreover, the number of majority class  samples employed for training was not fixed and was the same as the number of expanded minority class ones.The presented CNN-based fault diagnosis model was trained using the above dataset, and the trained model was evaluated through the test set. The CNN diagnosis model comprised five alternately connected convolutional and pooling layers, two fully connected layers, and a SoftMax classifier. The size of the convolutional kernel was 3 × 3, the number of convolutional kernels was 16, 32, 32, 64, and 128; for the pooling layer, 2 × 2 maximum pooling was used; the activation function was ReLU; and the number of nodes in the fully connected layer was 1024 and 256. In order to avoid the network from overfitting, a BN layer was added after the first convolutional layer, and a dropout operation was added after the fully connected layer, where the value was set to 0.25. The model was trained with a batch_size = 64 and an epoch = 500, and the learning rate was chosen as 0.001. The model utilized a zero-padding method to increase the dimensionality so that the feature map size remained the same before and after convolution. In the training process of CNN, the weights and biases of the model first had to be initialized using small random numbers. Then, the data were passed from the input layer all the way to the output layer through a forward propagation process. During forward propagation, CNN extracted image features through a series of convolution operations, activation functions, and pooling operations. After that, the output of the forward propagation was compared with the real label, and the value of the loss function was calculated. Next, CNN calculated the gradient of the loss function with respect to the model parameters via backpropagation, and the Adam optimization algorithm was used to update the model parameters so that the value of the loss function gradually would approach the optimal solution. Finally, the process of forward propagation, computation of losses, backpropagation, and parameter updating was repeated until a preset number of training rounds was reached, or certain convergence conditions were achieved.In order to diminish the effect of chance errors, all datasets were tested 10 times, and their diagnostic efficiency was evaluated through the mean precision of the 10 test results. Considering the above experiments, Experiments 1–3 only involved the original real samples, Experiments 4–19 were conducted using the generated samples for data augmentation, and Experiments 20–22 were conducted using only the generated samples for training the diagnostic model.The analysis of the results of Experiments 1–3 and Experiments 20–22 indicates that the efficiency of the FD model trained on the produced samples only is lower than that using the real samples only. The reason for this is that the generated sample is extracted from the actual sample, which only supplements the real sample and cannot completely replace it. Moreover, it can be concluded from Experiments 3, 19, and 22 that the diagnostic accuracy is low for less than 500 training samples, indicating that the number of training samples considerably affects the diagnosis model’s precision.The comparison of the results of Experiment 1 with Experiments 6–12 and Experiment 2 with Experiments 16–18 indicate that the diagnostic accuracy can be effectively improved by adding additional produced samples to the training sample set for sample enhancement in FD model training. When the ratio of the number of generated samples to the number of real samples is between 0.25 and 1, there is a considerable improvement in the diagnosis model’s accuracy. The best improvement in diagnostic precision can be attained when the number of generated samples is half the number of real ones. Data enhancement using generated samples for unbalanced datasets is an effective method when performing FD under sample imbalance situations, and the efficiency and feasibility of the presented approach are also illustrated.However, the number of added generated samples is not as high as possible during sample augmentation. The results of the comparisons of Experiment 1 with Experiments 4–7 and Experiment 2 with Experiments 13–15 show that adding more generated samples than real samples cannot significantly affect the diagnostic model and enhance diagnostic efficiency. The accuracy of the diagnostic model is reduced when adding too many generated samples. This is because the generated samples contain other information about the real samples’ characteristics and generation errors. Excessive interference can degrade the diagnosis model’s efficiency, further illustrating that the generated samples cannot completely replace the real ones.In order to further assess the feasibility of adding produced samples to the imbalanced sample set for data augmentation in FD and the efficiency of the presented approach, the proposed method was validated via experiments.In these experiments, different sample generation methods were first used to supplement the data of the unbalanced sample set shown in The above dataset was employed to train the CNN diagnosis model, while its architecture and parameter settings were kept unchanged. Meanwhile, to evaluate the efficiency of the presented approach, other diagnosis models, like the support vector machine (SVM), random forest (RF), and BP neural network, were trained based on the same dataset for comparison. The CNN diagnosis model used in this paper adopted the SDP image directly as the input, while the SDP image’s texture feature parameters were chosen as the inputs of other diagnosis models. The accuracy of different datasets in different fault diagnosis models was then evaluated using the test set. In order to alleviate the impact of chance errors, each method was tested 10 times, and their diagnostic efficiencies were evaluated through the mean precision of the 10 test results, as presented in The experimental results indicate that the classification precision of all fault diagnosis approaches on the mixed dataset with the addition of the produced samples is superior to the unbalanced dataset. The reason for this is that the hybrid dataset has more image samples that can be employed for training. This reveals that when performing FD under the sample imbalance condition, using the sample generation method to generate additional minority class samples for supplementing the imbalanced dataset can effectively increase the classification precision of the FD model and solve the low diagnosis precision problem caused by the lack of training samples.Meanwhile, the test results of all the fault diagnosis models on the mixed sample set indicate that generating a few classes of samples for data supplementation using the DDPM method can improve the diagnosis classification precision compared with other generation methods. This reflects that the images generated via the DDPM method have better quality, with higher similarity to the real images and a better enhancement of the original unbalanced data. The DDPM-based sample generation method can be utilized as an efficient data enhancement method to promote the diagnostic precision and generalization efficiency of FD classifiers under unbalanced sample conditions.The experimental results also indicate that the presented CNN FD model achieves the highest diagnostic precision on all datasets, which is because the CNN model is a deep neural network. Compared with the other three shallow networks, CNN is able to extract more and deeper characteristics to efficiently characterize the complicated mapping relationship between RB vibration signals and fault states. Another reason for this is that the input of CNN is an SDP image, which can be better characterized for rolling bearing faults. At the same time, the inputs of SVM, RF, and BP neural networks are the SDP images’ texture feature parameters, which are subject to uncertainty caused by human interference in the extraction procedure, and some critical fault features may be lost.In order to further illustrate the efficiency of the developed approach, The classification effect of the CNN FD model in a mixed dataset is considerably better than that on the unbalanced dataset. Enhancing the unbalanced dataset using DDPM-generated image samples can improve the diagnostic model’s classification precision in unbalanced situations. Meanwhile, the mentioned results also indicate that the FD method using the SDP images, DDPM, and CNN model presented in the current work can accurately classify rolling bearings’ different operating conditions in the sample imbalance conditions, demonstrating the efficiency of the presented approach.In order to illustrate the advantages of the SDP images used in this paper for the rolling bearing fault diagnosis task, we also converted the vibration samples of rolling bearings into vibration grayscale images, short-time Fourier transform (STFT) images, and wavelet images, which are commonly used in fault diagnosis research, and carried out the comparison experiments. In the experiment, the corresponding types of image samples were firstly generated using DDPM, and then a mixed dataset was constructed by utilizing various types of real images and generated images, and the number of all samples in the dataset was set in the same way as that of the SDP images in From the results, it can be observed that the CNN diagnostic model has the highest accuracy when using SDP images as input. This is followed by the wavelet image, STFT image, and grayscale image, respectively. This is due to the grayscale image only being obtained based on the amplitude of the vibration signal, and some fault features may be lost during the conversion process. The SFTF image allows for the analysis of the signal through a fixed-size time window, which has a limited time-frequency resolution. Wavelet images, as a result of the wavelet transform, may have boundary effects and frequency aliasing. Moreover, the wavelet transform needs to set the basis function in advance, which cannot meet the adaptive requirements of the data and has limitations in dealing with nonlinear unstable signals. Compared with the above images, an SDP image only converts the vibration signal into an SDP image in polar coordinates, as SDP images are able to contain more information concerning fault characteristics and are more suitable for carrying out rolling bearing fault diagnosis tasks.In order to further illustrate the advantages of deep learning methods in fault diagnosis, as well as to further validate the robustness and effectiveness of the proposed methods, we conducted experimental comparisons by utilizing some widely used deep learning-based fault diagnosis models in the field of fault diagnosis. The datasets used in the experiments were all mixed datasets with sample balance reached after data supplementation using DDPM-generated images. In order to eliminate the effect of chance errors, each method was tested 10 times, and diagnostic performances were evaluated using the average accuracy of the 10 test results, which are shown in In the experiments, the architecture and parameter settings of the CNN diagnostic model remained unchanged. CBAM-CNN was added to the CNN model with the CBAM attention mechanism. CBAM incorporated both channel attention and spatial attention, which allowed the model to focus on regions where fault characteristics were more pronounced. The 1D-CNN model consisted of five convolutional layers, two fully connected layers, and a SoftMax classifier. The input for the 1D-CNN was a 1 × 2048 vibrating sample; the size of the convolutional kernel was 1 × 32; the number of convolutional kernels was 16, 32, 32, 64, and 128, respectively; the activation function was ReLU; and the number of nodes in the fully connected layer was 1024 and 256. The diagnostic model based on the deep belief network (DBN) consisted of one input layer, one output layer, and four hidden layers. The number of nodes in the hidden layers was 512, 256, 128, 64, and 32, and the hidden layer activation function was Sigmoid. The diagnostic model based on the stacked autoencoder (SAE) consisted of one input layer, one output layer, and four hidden layers. The number of nodes in the hidden layers was 512, 256, 128, 64, and 32, and the hidden layer activation function was ReLU.From the experimental results, it can be seen that the CNN diagnostic method used in this paper outperforms other deep learning diagnostic methods in terms of accuracy, reaching 99.45%. This indicates that the method proposed in this paper has significant advantages over other deep learning diagnostic methods. When using the same vibration image as input, the diagnostic accuracy of CNN is significantly higher than that of DBN and SAE. This is because CNN is better able to extract the local features and spatial information in the image, which is more suitable for processing image data. Also, the diagnostic accuracy of CBAM-CNN and 1D-CNN is higher than that of DBN and SAE, which indicates that the CNN model is more capable of fault feature extraction. In addition, because the 1D vibration signals are weaker than vibration images in characterizing fault features, the diagnostic accuracy of 1D-CNN with 1D vibration signals as input is 1.72% lower than that of the method proposed in this paper. Moreover, although CBAM-CNN adds a CBAM attention mechanism to the foundation of CNN, the diagnostic accuracy of the CBAM-CNN is not significantly different from that of CNN in the proposed method in this paper or is even slightly decreased. This indicates that the sensory field of the CNN model was sufficient for fitting the target features to the data before adding the attention mechanism, so the diagnostic performance of the CNN did not change significantly after the addition of the attention mechanism. In addition, adding an attention mechanism increases the model parameters, which may increase the likelihood of overfitting in the CNN model.It is worth noting that the diagnostic results reveal that the accuracy of all deep learning fault diagnosis methods is higher than 95%. This is because, compared with traditional shallow diagnostic models such as BPNN and SVM, deep learning models do not need to extract fault features manually, thus reducing the uncertainty of manual feature extraction. It also indicates that deep learning models have stronger feature extraction and processing capabilities, which makes them more suitable for fault diagnosis research.Moreover, we also compared the standard deviations of the diagnostic accuracy of the different methods. The standard deviation of the accuracy rate can reflect variations in the diagnostic model’s performance in different experiments or different datasets and can indicate the robustness of the diagnostic model to some extent. From the experimental results, it can be found that the CNN diagnostic method proposed in this paper has the smallest standard deviation of accuracy, which indicates that the model has the most stable performance in multiple fault diagnostic experiments and has a strong ability to adapt to different image samples, which also indicates that the method has strong robustness.Finally, we also compared the training time of different deep learning fault diagnosis methods. From the results, we can see that the training time of the CNN diagnostic model proposed in this paper is relatively long, but it is still within the acceptable range. The differences between the training times of CBAM-CNN, DBN, and SAE compared with CNN may be caused by the number of parameters in the model, with models with fewer parameters taking relatively less time to train. Since CNN is more suitable for processing image data, the training time of CNN will be slightly shorter than 1D-CNN. However, the training time of the fault diagnosis model is affected by many factors, such as the optimization algorithm, data size and quantity, and hardware equipment. Therefore, the training time and diagnostic performance of diagnostic models need to be evaluated in combination with specific diagnostic tasks and experimental environments.When applying this method in actual working conditions, it is first necessary to carry out the long-time condition monitoring and operation status acquisition of the target equipment. The acquired normal data with a small volume of fault data were converted into SDP images. The fault images were then used to train the DDPM and enable it to generate images with similar fault characteristics as the real fault images. Next, the faulty images were supplemented using the generated images so that the number of faulty images was the same as the normal images. Finally, the CNN fault diagnosis model was trained using the balanced dataset so that it could extract the fault features and implement fault diagnosis.In order to investigate the efficiency and applicability of the developed approach, its experimental verification was performed using the rolling bearing RB fault data of the primary mine fan under actual working conditions.3/min.The primary mine fan rolling bearing data from an FBC(D)Z type primary mine fan in a coal mine mainly comprised a flow collector, a motor, an impeller, a diffuser, a muffler, a diffuser tower, and other structures, as presented in Since the vibration signal generated using the RB of the fan during operation significantly depends on the type, degree, and position of the mechanical fault of the fan, it contains a wealth of information about the equipment’s operating status. Moreover, the vibration signal-based FD approach has the advantages of easy acquisition and broad applicability. Thus, the vibration data of the fan’s RB were acquired using a vibration pulse sensor and data acquisition system, as shown in Sensors were installed on the driving and nondriving ends of the primary mine fan motor to collect vibration signals from the rolling bearings during operation. In actual working conditions, the sensor’s sampling rate was 10 k, the resolution ratio was 0.625, the number of sampling points was 16 k, the sampling time was 1.6 s, and the signal was collected at three-hour intervals. In order to facilitate the work of repair personnel in the field, monitoring software was utilized to monitor and display the collected vibration signals. In order to evaluate the data’s truthfulness and usability, we performed signal analysis on the normal and faulty vibration signals after assessment using monitoring software. As shown in A frequency (40 Hz) close to the theoretical fault characteristic frequency After monitoring the long-term operating conditions of the mentioned primary mine fan, the rolling bearings’ vibration signals were acquired under various operating conditions. Since the fault of the RB in the primary mine fan is derived from various factors, and the primary mine fan is different from other mechanical equipment, its ability to work properly is vital to the safety of coal production. Therefore, it must be shut down for repair once a primary mine fan fault occurs. According to the application requirements of the actual working conditions, it is only necessary to diagnose whether a fault occurs in the primary mine fan’s RB, and it is not necessary to diagnose the specific type of fault. Accordingly, we collectively defined the multiple fault operation data collected as fault status data and classified the primary mine fan rolling bearing operation data into normal and fault status. The normal and fault-state data were the majority and minority class data, respectively. First, the preprocessed bearing vibration signal was sample-segmented using the overlapping sampling method, and each sample included 512 sampling points. The SDP approach was then utilized to transform the RB vibration signal into a 2D SDP image, and the SDP image conversion parameters were similar to those outlined in the previous section. The obtained SDP images were categorized into training and test samples, and an unbalanced sample set was established, as shown in It can be observed that the presented DDPM method generates images that outperform other generation methods in all evaluation criteria. This indicates that the DDPM-generated images are of high quality and that the produced images are of similar appearance to the real images and have similar feature distributions. In order to further describe the benefits of the DDPM approach in generating images, the t-SNE approach was utilized to visualize a partial real and generated image of the primary mine fan rolling bearing fault state in a dimensionality reduction. The visualization results of the sample distribution were utilized to verify the similarity between the produced and real images. The results are presented in As presented in In order to further verify that adding generated samples to the unbalanced dataset for data enhancement helps to enhance the classification precision of the RB FD model of the primary mine fan, the proposed method was validated via experiments. In the experiments, different generation methods were used to supplement the data of the imbalanced dataset shown in The presented CNN diagnostic model was trained using the mentioned sample set, and the CNN diagnostic model’s architecture and parameter settings were similar to those outlined in the previous section. In order to verify the efficiency of the developed approach, other FD models like SVM, RF, and BP neural networks were trained using the same dataset for comparison. The CNN FD model adopted the SDP image directly as input, while the inputs of other diagnostic models were the SDP image’s texture feature parameters. The accuracy of different datasets in different fault diagnosis models was tested using a test set. In order to alleviate the impact of chance errors, each method was tested 10 times, and their diagnostic efficiencies were evaluated through the mean precision of the 10 test results, as described in The experimental results indicate that the precision of the FD model in the mixed dataset is higher than that in the unbalanced dataset. This indicates that adding the generated samples to the unbalanced dataset of the primary mine fan rolling bearing for data enhancement considerably improves the precision of the FD model. It efficiently solves the problem of insufficient diagnostic accuracy induced by the unbalanced number of samples when the FD model is applied to the actual working conditions. Meanwhile, the experimental results indicate that the FD model has a higher diagnostic precision in the mixed dataset with DDPM-generated samples added than in the dataset with other generated samples added, indicating that using the DDPM method for generating a few classes of samples is a more efficient approach for data enhancement. This is because the DDPM method is able to fit the RB fault image of the primary mine fan more effectively when generating image samples, generating an image containing more effective fault features.There is a considerable decrease in the accuracy of all diagnostic models on the primary mine fan’s rolling bearing dataset compared with the rolling bearing test set in the previous section. This is because the operating environment in the actual working conditions affects the primary mine fan, and the bearings of the primary mine fan are also subject to additional loads from the impeller and airflow. Therefore, the operating vibration signal of the RB of the primary mine fan would contain much interference noise, degrading the diagnostic precision of the FD approach. However, the CNN diagnostic model employed in the presented primary mine fan rolling bearing FD method outperforms the other commonly used diagnostic models in all datasets. Compared with the original unbalanced dataset, the presented approach improves the final FD accuracy by up to 5.9% after data enhancement. This indicates the feasibility and efficiency of both the DDPM and CNN approaches employed in the presented approach.In order to further validate the effectiveness and robustness of the proposed method in real working conditions, we conducted comparative experiments using other commonly used deep learning fault diagnosis models. All the fault diagnosis methods had the same model architecture and parameter settings as those given in the previous section. The dataset used was also the same mixed dataset with the sample balance reached after data supplementation using DDPM-generated images. In order to eliminate the effect of chance error, each method was tested ten times and their diagnostic performances were evaluated using the average accuracy of the ten test results, which are shown in From the analysis of the experimental results, it can be seen that the accuracy of the CNN-based fault diagnosis method proposed in this paper is significantly higher than that of other deep learning diagnostic models, reaching 98.13%. Compared with DBN with SAE, the diagnostic accuracy of CNN is 6.1% and 3.96% higher, which indicates that the CNN-based diagnostic method is superior to the DBN with the SAE-based method in terms of diagnostic performance. Meanwhile, the diagnostic accuracy of the CNN-based method is 3.43% higher than that of the 1D-CNN-based method, and this result likewise illustrates the greater advantage of fault diagnosis research based on vibration images and the superiority of the proposed method in this paper. Moreover, the diagnostic accuracy of the CNN diagnostic model before and after the addition of CBAM does not differ much, indicating that the CNN model is sufficient for fitting the target data for the current diagnostic task, so its diagnostic performance remains basically unchanged.Comparing the experimental results with the diagnostic results of conventional rolling bearings in the previous section, all deep learning diagnostic methods showed a decrease in accuracy for the rolling bearing data in the primary mine fan, especially the 1D-CNN-based diagnostic method. This is due to the presence of a large amount of interfering noise in the vibration signals of the rolling bearings in the primary mine fan, which masks some of the fault characteristics, thus affecting the diagnostic performance. However, the diagnostic performance of the proposed method still achieves satisfactory results, which validates and illustrates the effectiveness and robustness of the proposed method in this paper. Meanwhile, the accuracy of other methods is also higher than 90%, which illustrates the superiority of deep learning methods in fault diagnosis research.To further validate the robustness of the proposed methods, we calculated the standard deviations of the accuracy rates of all diagnostic methods over multiple experiments. The results show that the CNN diagnostic method proposed in this paper has the smallest variation in accuracy rate over multiple experiments, and its standard deviation is also the smallest. This confirms that the proposed method is robust, and it has a strong adaptive ability in dealing with different data or in the presence of noise and still maintains a relatively stable diagnostic performance.Moreover, it can be seen from the training time of all fault diagnostic models that the training time of diagnostic models increases when dealing with real data under actual working conditions. The reason for this phenomenon may be the presence of a large amount of noise interference in the real vibration signal, which can increase the difficulty of the model when extracting fault features. However, the training times of CNN and other deep learning fault diagnosis methods are within acceptable limits, indicating that deep learning methods are more suitable for fault diagnosis tasks under certain conditions. The analysis results reveal that the FD approach using the SDP image, DDPM, and CNN model presented in the current work can precisely detect and classify the operation status of the rolling bearings in the primary mine fan. This demonstrates the efficiency of the presented approach and shows that it can be appropriately applied to the condition monitoring and FD of the primary mine fan rolling bearings under actual working conditions.The current study presents a primary mine fan rolling bearing FD approach using SDP images, the DDPM image generation method, and the CNN, which are able to accurately identify the primary mine fan rolling bearing fault under sample imbalance conditions. Using the SDP method, the 1D vibration signal was first converted into a distinctly characterized 2D SDP image. The DDPM method was then utilized to generate images with similar feature distributions to a minority class of real fault images, which were then added to the imbalanced dataset for data enhancement. Finally, taking advantage of the CNN in image processing, the CNN was adopted as a fault diagnosis model to identify and classify the working situations of RBs.In order to assess the efficiency of the presented approach, it was validated using a conventional RB fault test bench dataset in a laboratory environment and the real operating data of the primary mine fan rolling bearings under actual working situations. In the experiments, the correlation coefficients between the produced and actual images were determined to investigate the optimal training rounds of DDPM. The quality of DDPM-produced images was then evaluated quantitatively using various evaluation metrics and compared with other popular image generation approaches. Finally, the effects of the number of generated samples on the precision of the FD model under various sample imbalance rates and the effects of data enhancement on the FD precision were compared and analyzed using different image generation methods for imbalanced datasets.Although this approach attained relatively excellent results in the sample imbalance FD of the primary mine fan rolling bearing, in future works, further areas should be explored, such as improving the sampling speed of the image generation method, enhancing the FD method’s anti-interference capabilities regarding the severe noise in real operating situations, and the research of a lightweight diagnosis model for various applications.


Flipped learning (FL) is a model which allows students to adjust their study rhythm by taking advantage of class time to apply their knowledge. Although FL meets many of the needs of the traditional lecture-based model and the exclusively virtual model, its effectiveness in medical residency programs has not been thoroughly studied. Our study evaluates the efficacy of an FL model in a cardiology residency program based on the satisfaction and academic performance of the participants.A prospective, observational, and descriptive study was conducted at Ignacio Chávez National Institute of Cardiology in Mexico City to evaluate the satisfaction and effectiveness of an FL model for acquiring competencies in electrocardiographic diagnosis among thirty-one first-year cardiology residents. The Advanced Electrocardiography Workshop and a virtual classroom were designed for the intervention. Four teachers taught the workshop and video classes, and ten medical specialists from the participants’ areas of work performed evaluations of practical skills before and after the intervention.P value < 0.001).75% of the participants rated the intervention as very favorable compared to the traditional and virtual models. The main advantages offered by the FL model were identified. An improvement of high statistical significance was observed in participants’ academic performance after the intervention (The FL model has a high degree of acceptance among participants and teachers. Our study shows an improvement in academic performance after the intervention. Considering that the FL model is flexible and reproducible in other areas of medicine, it represents an excellent educational alternative that meets current needs. Socratic questioning serves as the ultimate form of argumentative dialectic, with the teacher assuming a paternalistic role in guiding the disciple through discussions on a topic that is familiar to both but mastered by the teacher. This principle was later embraced by Plato in the establishment of the Academy. In The Banquet, Diotima compares the philosopher to a midwife, stating that “the soul is pregnant and wants to give birth,“ drawing an analogy between knowledge and childbirth. In contemporary idealism, the essence of the flipped learning (FL) model lies in the Socratic dialectic, where the student possesses knowledge of the subject and the teacher acts as a guide in facilitating knowledge discovery. Rather than merely transmitting knowledge, the focus is on teaching its correct application and reasoning .The traditional lecture-based model (LBM) directly provides the student with knowledge during class time, while time outside the classroom is dedicated to reinforcing, practicing, and reflecting on this knowledge. One of the negative points of this model has been the mismatch between the student’s learning style and the teacher’s teaching style, which was the main problem that led to the proposal of alternative educational strategies during the second half of the 20th century and the beginning of the 21st century . Flippedversus LBM showed heterogeneous results; although some found a significant difference [The success of this model and the ease of its reproducibility catapulted its use in various educational contexts; medical education was no exception. Multiple studies have evaluated FL’s effectiveness in acquiring medical knowledge in undergraduate and graduate medical education. Studies that compared knowledge and skills acquisition through the FL model fference –10, othefference , 12. In fference –12. Althfference .Cardiology teaching has recently integrated new technological tools for acquiring and reinforcing knowledge . HoweverFL is a tool applicable to the hospital environment that meets the needs posed by the limitations of LBM in medical education. The objective of this study was to evaluate the satisfaction rates and effectiveness of an FL model among first-year cardiology residents.A prospective, observational and descriptive study was carried out to evaluate the satisfaction and effectiveness of a flipped learning model among first-year cardiology residents for the acquisition of electrocardiography competencies. It was carried out in the facilities and with the participation of the academic staff of Ignacio Chávez National Institute of Cardiology of Mexico City. Prior to the start of the intervention, all participants were evaluated through an examination of theoretical knowledge in electrocardiography. In turn, medical specialists from the participants’ areas of work individually assessed the performance of 5 core competencies in electrocardiography Table . For theTo evaluate academic progress, at the end of the intervention, an examination of theoretical knowledge and an evaluation by the medical specialists regarding the acquisition of the five competencies was carried out individually. Participants and teachers were surveyed to determine their satisfaction with the FL model.A total of 35 first-year cardiology residents were invited, all of whom were medical graduates who had completed training in internal medicine as a prerequisite and were enrolled in the first year of cardiology residency during the third quarter of 2021. We had the support of 4 teachers who conducted the AEW and the asynchronous video classes; all of them are cardiologists with extensive experience in electrocardiography. They monitored the residents during the mentoring, training, and feedback process. Additionally, ten medical specialists from the participants’ areas of work objectively carried out personalized evaluations of the five core competencies in electrocardiography before and after the intervention.Canvas  was used for the development of the virtual classroom. It is worth noting that the platform is open access, allowing both users and developers to freely access and utilize its features, it also allows the establishment of two-way communication channels. The academic content was divided into six modules based on the topics evaluated in the first exam . We confirmed the normal distribution of the results obtained from the pre and post intervention examinations using a Shapiro-Wilk test . To determine the statistical significance of the results we employed a t-test. The workshop included 13 academic sessions distributed over eight weeks. As the first activity, the sessions included introductory gamification activities assessable through Kahoot! , where the knowledge acquired through the video classes was applied. The first 15 min of each session were allocated to this activity. The remaining time (65 min) was used to perform ECG analyses based on the presentation of clinical cases according to the corresponding topic. Each academic session lasted 80 min.A rubric was created wherewith medical specialists personally evaluated the five core competencies in electrocardiography before and after the intervention; based on their judgement, the physicians indicated whether the participant had that competency.very satisfied, satisfied, neutral, dissatisfied, and very dissatisfied. A 3-part survey was applied to participants in which two questions asked them to express their degree of satisfaction with the intervention compared to the LBM and VM. There were four response options for these questions: very favorable, favorable, neutral, and unfavorable. The third part of the survey asked the participants to select the FL model characteristic they considered the main advantage over the LBM. Four options were provided: less theoretical content in face-to-face classes, asynchrony in the review of theoretical content, face-to-face interaction after reviewing theoretical content, and having the support of the theoretical content. The intervention lasted eight weeks.The degree of satisfaction of both the participants and teachers was evaluated through a comparative analysis of the LBM, the VM, and the FL model. The teachers were asked about their degree of satisfaction with the FL model, with five responses: Thirty-one residents voluntarily agreed to participate in the study, of whom 25 were male (80.5%) and six were female (19.5%). Throughout the intervention, good involvement was obtained among participants and teachers, reflected in the number of visits to the platform and the satisfaction results. Five thousand nine hundred seventy-two visits were recorded in a virtual classroom over the eight weeks, an average of 192.6 visits per student.Three of the four teachers (75%) expressed being very satisfied with the FL model, while one of them (25%) described being satisfied.Twenty-three of the thirty-one participants (74.1%) rated the intervention as very favorable compared to the LBM, four as favorable (12.9%) and four as neutral (12.9%). Compared with the VM, twenty-eight participants (90.3%) rated it as very favorable and four (9.7%) as favorable.Classroom interaction after reviewing the technical content; 8 participants (25.8%) chose Available support from the technical content, while 5 participants (16.1%) elected Asynchrony in the review of the theoretical content. Finally, 2 participants (6.4%) selected Reduced content load during class as the FL’s primary benefit.  voted in favor of Gamification activities were not considered during the analysis of academic progress data but instead served as a self-assessment method for the participants.P value < 0.001) showing thus a statistically significant improvement  and the average score on the test after the intervention was 90.1 , an increase of 21%  at the end of the intervention it was present in twenty-seven (87%); Competence 2 was already present in twenty-nine (93.5%), Competence 4 in twenty-six (83.8%) and Competence 5 in twenty-seven (87%), at the end of the intervention the 100% of participants acquired the competencies.Based on the criteria of the medical specialists, satisfactory results were obtained regarding the progress of the participants in the five core competencies in electrocardiography. Of the thirty-one participants Evaluation of Electrometry (Competence 3) did not differ before and after the intervention, as 31 participants had already acquired this competency  made the case analysis sessions dynamic and attractive for the participants while providing a practical and attractive self-assessment system.Numerous studies have conducted assessments on the effectiveness and extensive adoption of gamification tools within medical residency programs, resulting in a positive impact on students’ motivation and commitment , 21. ThiP value < 0.001); the evaluations support this finding by a third party  and by the authors . We consider the success of an FL model not only depends on reversing the traditional teaching and learning modality but also on aspects related to logistics, the quality of teaching, and the commitment of the student and teacher. Since a medical residency program hinders synchronous learning models, logistics plays a fundamental role in designing a structured program that allows participants to allocate time for reviewing the subject outside of the classroom and for applying knowledge during class, which is essential for the success of the intervention. A failure in this aspect could limit the reproducibility of the model. Some studies have highlighted that the effectiveness and students’ engagement with the FL model in medical education are determined by a complex interaction of factors. These factors include variations in teaching styles, individual learning preferences, the level of guidance during self-study, and the motivation of both teachers and students. It is important to note that most of these observations primarily derive from studies conducted in undergraduate medical education [Unlike the satisfaction results, the acquisition of knowledge and skills in the FL model has been challenging to reproduce, generating heterogeneous results among the multiple studies that have applied this model to medical education. In this study, knowledge acquisition was significantly improved compared to the results obtained before the intervention  for our FL model enabling us to provide the intervention to our participants free of costs. Considering the wide range of open access options available on the internet, the implementation of FL emerges as a convenient and cost-effective educational alternative.In terms of cost-effectiveness, we used an open access platform (One of the benefits of using a flipped learning model in medical education is that the time invested by the instructors in creating recorded lectures is equivalent to the time spent in delivering traditional in-person lectures. However, with recorded lectures, the instructors have the advantage of having the content available on the platform for students to access at any time and as many times as necessary. This can be particularly beneficial for students who may need more time to review the material, or for those who want to review the lecture before an exam or clinical rotation. While the upfront time investment may be greater for instructors in creating recorded lectures, the potential long-term benefits for students can make it a worthwhile investment. Additionally, the flexibility of the flipped learning model can allow instructors to spend more time engaging with students during in-person sessions, further enhancing the educational experience. The benefits of a flipped learning model extend beyond the current course and can be utilized for future classes as well. Once the recorded lectures are created, they can be reused for future classes, reducing the time and effort required for instructors to prepare lectures.FL is a reproducible educational strategy applicable to teaching in medical residency programs, with high satisfaction levels reported by students, however, its success in knowledge acquisition depends on multiple factors. The FL model is particularly attractive and viable today, as it provides physicians-in-training with a flexible and personalized model that overcomes the limitations of traditional and virtual learning. The intervention confirmed that having undergone training based on the FL model was helpful for acquiring skills and knowledge in electrocardiography among cardiology residents. Although the LBM continues to dominate medical education programs, it is imperative to continue implementing new models that meet current needs and integrate technological resources that facilitate the learning experience. When well applied, FL is a successful alternative for teachers and students.Our study has some limitations that should be considered. First, due to the practical challenges, particularly when involving medical residents, our study did not include a control group. Second, our study did not assess the long-term retention of ECG interpretation skills. Therefore, it is unclear whether the improvements observed in our study are sustainable over time. Future studies should consider including a control group and evaluating the long-term retention of ECG interpretation skills to better understand the efficacy of the flipped learning model in medical education.


The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection. Predictive maintenance of wind turbines is a critical aspect of wind energy management that involves using data analysis and machine learning techniques to predict when maintenance tasks will be required for wind turbines. It is important because it helps wind farm operators reduce the downtime and the repair costs, increase the operational efficiency of their wind turbines, and ensure the safety of their workforce. With the increasing global demand for clean energy and the growing number of wind turbines in operation, predictive maintenance has become a vital tool for wind energy companies to improve their bottom line and meet their sustainability goals. Furthermore, as predictive maintenance technology continues to advance, wind farm operators can remotely monitor their turbines, reducing the need for manual inspections and improving safety for maintenance workers.Several components of wind turbines are known to fail more frequently than others, including the following: gearboxes, bearings, blades, and control systems.Gearboxes are critical components that convert the low-speed rotation of the blades into high-speed rotation for the generator. Due to the high stresses and heavy loads involved, gearboxes are prone to wear, overheating, and oil contamination, which can cause failure. The bearings support the rotor and other rotating parts of the wind turbine. They are subjected to high loads and rotational speeds, making them prone to wear and damage. The generator, as a key component of the wind turbine, converts mechanical energy into electrical power. The high value generator accounts for about 10% of the total cost of the wind turbine ,3.Wind turbine blades are exposed to extreme environmental conditions, including high winds, temperature changes, and lightning strikes. Over time, this can lead to blade erosion, cracking, and other forms of damage that can affect their performance. The control systems of a wind turbine are responsible for regulating its operation and ensuring that it operates within safe and optimal parameters. If these systems fail or malfunction, the turbine may be shut down or run at suboptimal levels, leading to reduced efficiency and increased maintenance costs.The application of predictive maintenance techniques is desirable because operators can monitor these and other critical components of their wind turbines to identify potential problems before they occur, allowing them to take proactive steps to address them and avoid costly downtime.The global installed capacity of wind power has increased from approximately 2.5 GW in 1992 to almost 236 GW at the end of 2021. The plan is to install 116 GW of new wind farms in Europe over the period 2022–2026, which is 23 GW a year on average ,5. SinceIn this paper, we focus our investigation on machine learning techniques that could be efficiently applied for the analysis of wind turbine sensor data and to indicate anomalous sensor measurements related to the possible failure of the wind turbine components. The anomaly detection-based approach in this paper is also used to indicate how many days in advance we may expect to detect the first signs of abnormal conditions in the components of the wind turbine, which will be later followed by failure.It is important to predict wind turbine failure as early as possible because it allows wind farm operators to plan and schedule maintenance tasks in advance, minimizing the downtime and reducing the risk of costly repairs or equipment replacement. By predicting failures in advance, operators can order replacement parts, allocate resources, and plan maintenance work without disrupting the operation of the wind farm or risking the safety of maintenance workers.In addition, predicting failures in advance can help wind farm operators optimize their maintenance schedules and reduce maintenance costs. Rather than relying on scheduled maintenance at fixed intervals, predictive maintenance enables operators to perform maintenance only when it is needed, based on the actual condition of the wind turbine components. This can help extend the useful life of the equipment, reduce repair costs, and improve the overall efficiency of the wind farm.Finally, predicting failures in advance can also help improve safety for maintenance workers. By identifying potential problems before they occur, operators can take steps to minimize the risk of accidents or injuries during maintenance work, ensuring that workers are able to perform their duties safely and efficiently.The remainder of the paper is organized as follows. In The field of predictive maintenance of wind turbines is rapidly advancing, with new technologies and techniques being developed to improve the accuracy and efficiency of predictive maintenance systems. In this section on the state of the art, we explore some of the latest developments in the field, including advances in machine learning algorithms, the use of sensors and IoT devices, and the application of big data analytics to wind turbine maintenance. We also examine some of the challenges that remain in the field, such as the need for more accurate data collection and analysis and the need for standardized approaches to predictive maintenance across different wind turbine models and manufacturers. Overall, this section provides an overview of the current state of the art in the predictive maintenance of wind turbines and highlights some of the most promising areas for future research and development.Most wind farms today are equipped with a Supervisory Control and Data Acquisition (SCADA) system, and the SCADA data are full of hidden information that can be utilized to detect faults early on. SCADA signal research has also been successful in establishing systems to identify wind turbine failures ,7,8,9. TBig data are promising for modeling wind energy, including wind speed forecasting ,12, poweThe authors of  stated tA data-driven decision-making methodology that combined prognostic and health management, proposed by , reducedA data-centric ML technology was proposed, where data-oriented steps were not a one-time procedure but were executed iteratively . It is iThe detection of anomalies in time series is a highly complex task that requires large data sets to accurately discriminate between points that exhibit normal and abnormal behavior . For exaThe authors of  claimed The experiments described in  showed tThe authors of  claimed The original unsupervised deep anomaly detection framework with the core of a neural architecture combining AEs and Graph Convolutional Networks (GCNs) was proposed by . The resThe majority of these algorithms were built in an artificial environment and were often not highly robust to the failure behavior of real-world systems, i.e., minor system failures . In most cases, even a small fault causes the engine to fail completely, resulting in the continuous reporting of an anomaly or the inability to detect additional anomalies .We used a public Energias De Portugal (EDP) wind turbine dataset from the Wind Turbine Failure Detection Challenge . This daUnbalanced data can cause many failures. In this case, we decided to monitor the most expensive part: the gearbox. In the following subsections, the different predictive maintenance approaches with this data set are reviewed.In this paper, we propose an approach that is less sensitive to the data-imbalance problem. We followed the hypothesis that the temperature of the gearbox bearing on the high-speed shaft correlated with all five failure types in the dataset and could be predicted in advance if we noticed unusual changes in the measured temperature.One challenge in temperature anomaly detection is dynamic temperature changes, which depend on the load of the gearbox and the current ambient temperature. Therefore, we could not use a fixed temperature level as an indicator of the anomaly. We decided to use a machine learning model for temperature prediction, which should continuously predict the temperature of the gearbox, and we compared it to the measured temperature. The model should be able to predict the temperature with a minimum error when all the turbine components are in good condition. This was achieved by training a machine learning model to map the measured sensor data to the gearbox temperature. Such a model always indicates what temperature we should expect in the current situation . If the model works correctly, and the real measured temperature is significantly different from the predicted one, then an anomaly of the temperature values is indicated, and we may predict the failure of the wind turbine component.According to the experience and knowledge of experts, we filtered out a set of ambient  and internal signals  to predict the gearbox bearing at a high-speed shaft temperature in a normal state when there were no failures in wind turbines. All these signals are listed in Selection of the input features for a temperature prediction model that sufficiently represented the influence of external factors on the gearbox bearing temperature when the turbine components were in a healthy state.Selection of the machine learning model to solve the temperature prediction task with a minimized regression error.Proposal of a technique for anomaly detection threshold selection when the predicted temperatures were compared with the actual measured ones.The implementation and practical application of our approach, proposed in this paper, needed to perform the following investigations:These features were sampled in the range of 3, 6, and 24 h. The selection of these particular time frames was guided by domain experience and the findings from previous research in the field of the predictive maintenance of wind turbines.Recurrent Neural Networks (RNNs) are a class of neural networks that are particularly suited for time-series data analysis. RNNs are designed to handle sequences of data with varying lengths and can capture temporal dependencies. This is essential when working with time-series data, such as wind turbine sensor data, as it allows the model to learn patterns that span across different time steps. In the study, the gearbox bearing temperature was predicted with three different models: Long-Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (bi-LSTM). These results are shown in Structures with a recurrent layer (128 cells) and two recurrent layers (128 + 100 cells) were tested.Training and testing were applied multiple times with different scenarios when the training data were selected from one turbine and tested with data from other turbines. In addition, multiple turbine data sets were combined and tested with the remaining data. The regression models were trained and tested 144 times to select the model with the best RMSE score.This study offers substantial contributions to the field of predictive maintenance and failure detection, particularly in the context of wind turbine operations. The successful implementation of a recurrent neural network, specifically a bidirectional long short-term memory (bi-LSTM) model, highlights the model’s ability to effectively predict temperature anomalies, serving as a potential indicator of imminent gearbox failure. This predictive capability allows for the timely mitigation of equipment failure, enhancing the efficiency and productivity while minimizing the downtime.The comparative analysis also revealed important insights. The superior performance of the two-layer bi-LSTM model over the other regression models reaffirmed the efficacy of recurrent neural networks in time-series prediction tasks. The models’ performances, however, were significantly influenced by the choice of input features. Shorter time ranges for the input features provided better predictive accuracy, underlining the importance of data granularity in time-series prediction. It achieved the lowest Root Mean Square Error (RMSE) at 1.61, indicating a strong predictive precision. This model also demonstrated the lowest standard deviation (Std) at 1.10, suggesting consistent performance. Furthermore, it had the smallest minimum error (Min) at 0.0021 and the smallest maximum error (Max) at 7.17, demonstrating its robustness in handling a variety of prediction scenarios. The results are displayed in Table 3The models were trained to predict the gearbox bearing at a high-speed shaft temperature in order to compare it with the actual temperature to find anomalies. Two automatic THD selection techniques were compared, based on three standard deviations from the mean value and based on the moving median. The effect on the early failure prediction is given in However, while our findings are promising, they also raise additional questions for future research. For example, how would the bi-LSTM model perform with different parameter configurations or in predicting other types of anomalies? Furthermore, while shorter time intervals proved beneficial in this context, further research is needed to establish the optimal time interval that balances the prediction accuracy and computational efficiency.In this investigation, we demonstrated the potential of recurrent neural networks, particularly a bidirectional long short-term memory (bi-LSTM) model, in effectively predicting temperature anomalies that may signify imminent failures in wind turbine gearboxes. Our model’s ability to accurately model the time series of a healthy device’s parameters has proven instrumental in detecting these anomalies by comparing the predicted values with actual measurements.The comparative analysis between various models and input features further established the superiority of a two-layer bi-LSTM model and the advantages of using shorter time ranges for input features (3 h). This outcome illuminates the importance of selecting an appropriate model and the right time granularity in time-series prediction tasks.Our approach using THD selection based on the moving median has shown promising results in facilitating the detection of anomalies 60% earlier than other techniques. This capacity for early detection underscores the potential of our method as an effective predictive maintenance tool, enabling the proactive mitigation of equipment failures and thus conserving time and resources.


In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors. In sound source localization (SSL), also known as acoustic direction of arrival estimation (DoA), the bearing angles (and in some cases range) of sound sources are estimated from the measurements taken by sampling the acoustic waves generated by the sources at different positions and orientation in space. SSL for wideband sources can be very computationally demanding due to the complexity of the existing algorithms. Thus, a low-complexity and computational efficient algorithm will greatly benefit embedded system real-time applications with a low-end microprocessor.Direction of arrival estimation finds application in speaker identification and localization ,2, sourcIn general, most real-life sound sources are wideband signals; hence, the narrowband assumption adopted for the development of the narrowband beamforming method does not hold. For wideband sources, the phase difference between the outputs of the sensors is not only dependent on the direction of arrival, but also on the temporal frequency. Some methods have been proposed to overcome this challenge. The wideband signal can be seen as a collection of multiple narrowband signals; hence, the narrowband beamforming methods can be applied to each narrowband bin and averaged over the entire wideband. These approaches are referred to as the incoherent signal subspace method (ISSM). Examples of such methods are the incoherent MUSIC (iMUSIC) , test ofAn alternative approach to wideband problems is the coherent method. In this approach, the covariance matrix of the component narrowbands is coherently summed into one narrowband covariance matrix, whereby the narrowband subspace method is applied to the universal covariance matrix. This is achieved by multiplying each narrowband cross-covariance matrix by a focusing matrix and then summed to obtain the steered covariance matrix. The earliest of this approach was proposed as the Coherent Signal-Subspace (CSS) method . Other cThe array manifold interpolation (AMI) method  is made Following the advances in the fields of embedded systems and Internet-of-Things (IoT), SSL and DoA methods are now being implemented on embedded systems. They are sometimes implemented together with other functionality, competing over the computational resources available on the embedded systems. Recent research in the fields of SSL and DoA has been conducted, where computational complexity is in focus, both on a model development level  and in mnth power of the ratio of the central processing frequency and the narrowband frequencies. Thus, the computational complexity reduction can be done by eliminating the calculation of the Bessel function. The simulation and experimentation results of the proposed method are compared with iMUSIC, WS-TOPS, and the original AMI method due to these particular existing methods do not require an initial estimate of the DoA [In this paper, a modified array manifold interpolation method is proposed for the uniform circular array of omnidirectional microphones. The proposed method takes advantage of the Bessel function’s asymptotic property and forms a UCA-specific focusing matrix. Combining the assumption of low-frequency wideband signals with an adequate signal-to-noise ratio, the proposed focusing matrix can be approximated as the product of a discrete Fourier transform (DFT) matrix and a diagonal matrix. The entries of the diagonal matrix are the  the DoA . The proThe rest of this paper is organized as follows: the signal model and array manifold interpolation are described in The signal model is described in L (either known a priori or estimated) number of wideband sources incident on a uniform circular array of radius r with M omnidirectional microphones, as shown in ℓth incident source. The bandwidth of the sources are not necessarily identical but must exist within some overlap bounded by mth sensor is given as:ℓth incident source, ℓth signal’s time-delay-of-arrival for the mth sensor and the reference point (center of the circular array), ℓth source, and mth sensor. Furthermore, in  of the mth sensor’s output:J is the total number of frequency samples, i denotes the imaginary unit, i.e., Considering given as:xm(t)=∑ℓ=Q is the number of independent measurements and qth measurement. Superscript The correlation matrix The correlation matrix of the observation has been developed for narrowband frequency od (AMI) . This memth element of the array manifold of the planar array is represented as a series expansion of a plane wave, i.e., the Jacobi–Anger expansion as:nth element of the row vector nth element the column vector n, mth microphone, and other variables are as previously defined.The N such that M total number of microphones is given by:The Bessel function decays exponentially as N and M such that For wideband sources, the concept of frequency smoothing is adopted to coherently sum the component narrowband covariance matrices of the wideband source into one narrowband covariance matrix. This is followed by the smoothing of the mapped narrowband covariance matrix. For this, we define an rding to  and 7)::M×M focusingular ,23.J is the total number of selected frequency samples as in atrix in  then becFinally, the steered covariance matrix is calculated using . Though n, and ‘−’ sign applies for odd n, respectively. The ratio in =Jratio in  becomes:M is the number of sensors, J is the number of narrow band frequency bins  . The eva(nlog2n) . The comation in  for T was taken. In this section, the performance of the proposed algorithm across The proposed algorithm is further tested for various sources. For the experiment, the following sound sources were used: car engine, water fall, hovering drone, electric fan, and train engine. For each sound, the source is moved to four locations, and the direction of arrival estimated using the original array manifold interpolation and the proposed modified method. The average estimation errors for each source type are reported in The performance of the proposed algorithm is shown to outperform the original AMI for all the sound types but the train engine, as shown in nth power of the ratio of the Bessel function of different orders are reduced to the nth power of the ratio of the processing frequency and the narrowband frequencies. This led to a significant reduction in the computation time for the diagonal matrix component of the focusing matrix, while achieving similar localization accuracy for low-frequency wideband sources compared to the original AMI method. This simplified algorithm eliminates the need for high-performance microprocessors for the implementation of real-time low-frequency wideband sound source localization on the embedded system. Furthermore, the proposed focusing matrix is independent of the array radius, making the focusing matrix reusable for the frequency bins if the signal spectrum remains fairly constant over time.For the uniform circular array, the steered covariance matrix method based on array manifold interpolation is more computationally efficient compared to other coherent covariance matrix averaging. This is due to the fact that the steering matrix in the array manifold interpolation method is independent of the source direction of arrival. It has been shown in this paper that by applying the small-argument approximation of the Bessel function, the