Existing material identification for loose particles inside sealed relays focuses on the selection and optimization of classification algorithms, which ignores the features in the material dataset. In this paper, we propose a feature optimization method of material identification for loose particles inside sealed relays. First, for the missing value problem, multiple methods were used to process the material dataset. By comparing the identification accuracy achieved by a Random-Forest-based classifier (RF classifier) on the different processed datasets, the optimal direct-discarding method was obtained. Second, for the uneven data distribution problem, multiple methods were used to process the material dataset. By comparing the achieved identification accuracy, the optimal min–max standardization method was obtained. Then, for the feature selection problem, an innovative multi-index–fusion feature selection method was designed, and its superiority was verified through several tests. Test results show that the identification accuracy achieved by RF classifier on the dataset was improved from 59.63% to 63.60%. Test results of ten material verification datasets show that the identification accuracies achieved by RF classifier were greatly improved, with an average improvement of 3.01%. This strongly promotes research progress in loose particle material identification and is an important supplement to existing loose particle detection research. This is also the highest loose particle material identification accuracy achieved to in aerospace engineering, which has important practical value for improving the reliability of aerospace systems. Theoretically, it can be applied to feature optimization in machine learning. As an important part of various pieces instruments, the reliability of sealed relays directly affects the reliable operation of aerospace equipment . Among mParticle impact noise detection (PIND) experiments can be used to effectively detect loose particles inside sealed relays ,9,10 andOne of the research directions of the authors is the material identification of loose particles inside sealed relays. On the basis of a sufficient literature review and experimental testing in the early stage, we propose to transform the loose particle material recognition problem into a multi-classification problem in machine learning. The theoretical basis for conducting research in this way is that if acoustic emission sensors were placed on the surface of a sealed relay, the sensors could collect the generated loose particle signals. Thus, if the materials of the loose particles were different, the loose particle signals generated by the collision would also be different. This would allow for analysis of the differences, which could be applied it to build a feature dataset and, ultimately, trained classifiers with different materials as labels. Specifically, we placed loose particles of different materials inside sealed relays and generated loose particle signals. The loose particle material dataset  can be formed by extracting the pulse area, spectral centroid, and other features that could reflect the material information from the signals. Combined with the Random Forest algorithm, the classifier was trained on it, and a detailed description can be found in Following this idea, in previous research, we extracted fourteen time-domain and frequency-domain features from loose particle signals and built a material dataset. We trained the classifier on the material dataset based on Decision Tree and Random Forest and concluded that the Random-Forest-based classifier (RF classifier) achieved the best classification results, i.e., achieved the highest material identification accuracy. The RF classifier achieved the highest material identification accuracy on the loose particle dataset of 59.63%. It should be noted that in the early stages, we focused on improving the classification performance of the classifier in algorithm selection and parameter optimization, which ignored the material dataset, especially the features and data distribution in the dataset. In machine learning, high-quality datasets are often more important than good classification algorithms . The feaIn machine learning, work related to feature processing of datasets is known as feature engineering. Many scholars have carried out research on feature engineering and achieved good research results. For example, Ma et al.  proposedBased on summarizing the relevant research results, we chose to shift our research focus to the features in the material dataset and thus proposed a feature optimization method of material identification for loose particles inside sealed relays. First, the missing values in the material dataset were processed, which is summarized as the missing value processing stage. We used a variety of statistical methods to fill in the missing values and applied the RF classifier to make predictions on the processed dataset. Thus, multiple identification accuracies were obtained. We used the direct-discarding method to process the missing values and applied the RF classifier to make predictions. By comparing the identification accuracies achieved by the RF classifier on the datasets processed by statistical method with those achieved by the direct-discarding method, the best missing value processing method can be obtained. Second, different standardization and normalization processing methods were used to deal with the problem of large difference in numerical distribution between features in the dataset, which was summarized as the standardization and normalization processing stage. Specifically, we used z-score standardization, min–max standardization, and row normalization to process the material dataset and applied the RF classifier to make predictions. The optimal standardization and normalization processing method can be obtained by comparing the obtained identification accuracies. Then, we studied the feature selection method based on the “filtering method” and designed a multi-index-fusion feature selection method, which is summarized as the feature selection stage. We used the new feature selection method to select features that contribute considerably to the identification accuracy of the RF classifier on the dataset and further constructed a new material dataset. The RF classifier achieved the highest identification accuracy on this new dataset. With this, we had obtained the best missing value processing method, the best standardization and normalization processing method, and the multi-index-fusion feature selection method and completed the design of the feature optimization method proposed in this paper. Finally, we constructed several material verification datasets to test the above feature optimization method. The practicability and robustness of the proposed method were proven by comparing the identification accuracy achieved by the RF classifier on the verification datasets before and after optimization.We used the DZJC-III PIND loose particle automatic detection system (loose particle automatic system), which was independently designed and manufactured by the LPDR group, to build the loose particle material identification experimental system, as shown in Based on the proposed loose particle material identification experimental system, the implementation processes of loose particle material identification are as follows.Step 1: Multiple sealed relay samples of the same type as the sealed relay (under test) were prepared in advance, and each sealed relay sample contain loose particles of only one type of material. Therefore, N sealed relay samples contain loose particles of N materials inside. We selected one of the sealed relay samples.Step 2: The selected sealed relay sample was fixed to the PIND experimental platform, and mechanical excitation was applied to it by driving the vibrating table so that the loose particle inside the sample would be in a collision or sliding state.Step 3: The loose particle signal collected by the acoustic emission sensor was sent to the loose particle detection system for conditioning and collection, and the collected signal data were sent to the host computer for storage.Step 4: The signal data were processed, and multiple features that reflect the material information were extracted from the processed signal data to form the material data, i.e., the data representing the material of the loose particle inside the sealed relay sample selected in Step 1.Step 5: The sealed relay sample selected in Step 1 was adjusted, in fact, it is to adjust the material of the loose particle contained inside the sealed relay. Step 1 to Step 4 were to obtain the data representing the new material. The material dataset representing different materials of loose particles was built by selecting the 1st to Nth sealed relay samples in order and repeating Step 1 to Step 4.Step 6: Multiple classifiers based on different machine learning classification algorithms were trained on the material dataset, the prediction performance of each classifier was evaluated, and the best-performing classifier was obtained. Then the inherent parameters were optimized to achieve optimal performance.Step 7: The sealed relay (under test) was fixed on the PIND experimental platform, and mechanical excitation was applied to cause the loose particle within to be in a collision or sliding state. Step 3 and Step 4 were repeated, the material data to be predicted (without labels) were compiled, and the optimal classifier was applied to make predictions. Then, the predicted loose particle material was obtained.In our previous research, we used the optimal RF classifier mentioned above. At present, the highest identification accuracy achieved by the RF classifier on the existing material dataset is 59.63%.Based on common loose particles found inside sealed relays, we chose copper wires, solder particles, aluminum particles, hot glue particles, wire coatings made of PVC material (PVC particles), wire coatings made of silica gel (silica gel particles)—a total of six kinds of materials—as the experimental objects. Considering the improvement in manufacturing sealed relays in recent years, a large number of test results show that the number of loose particles inside sealed relays was does not exceed three; therefore, our sealed relay sample contained one loose particle. The LPDR group coproduced, with Guizhou Space Appliance Co., Ltd., Guizhou, China, and obtained sealed relay samples containing loose particles of the abovementioned six materials. A model of the sealed relay samples, material information, and weight information about the loose particles are shown in Based on the obtained sealed relay samples containing loose particles of different materials, we used the loose particle detection system to conduct experiments. The experimental conditions were set in strict accordance with the Chinese GJB65B standard  and are We needed to select the weights of the loose particles contained in the sealed relay samples according to the sensitivity range of the acoustic emission sensor. In previous research, we tested whether the loose particle signal generated by loose particles of different weights could be collected by an acoustic emission sensor and obtained the weight range of the loose particles of the six materials used in the present experiment. Specifically, the weight range of silica gel loose particles is 0.6 mg to 1.6 mg, and the weight range of loose particles of other materials is 0.02 mg to 0.2 mg. It can be seen form the The loose particle signal generated by collision or sliding is an acoustic emission signal in a broad sense. From the perspective of acoustic emission signal identification, loose particle material identification is based on the principle of “inferring the acoustic emission source from the acoustic signal features”. Therefore, we selected the time-domain and frequency-domain features from the loose particle signal according to the above principle. First, the vibration frequencies generated by the collision of loose particles of different materials are different, and frequency-domain features, such as spectral centroid and Mersenne Cepstral coefficient, can be selected. Second, because the hardness of loose particles of different materials is different, the collision duration and the degree of close-to-elastic collision are also different. Time-domain features, such as pulse length and the degree of symmetry between left and right, can be selected. Because the masses of loose particles of different materials are different, the energies of the loose particles when they collide are also different. Time-domain features, such as pulse area and energy density, can be selected. In addition to the influence of the materials, the loose particle signal is also affected by the experimental conditions. Under different impact accelerations, vibration frequencies, and vibration acceleration conditions, the energies obtained by the loose particles detection system through different excitations are also different. Therefore, the experimental conditions can also be used as the loose particle signal features. It can be seen from Finally, we decided to extract a total of fourteen features from the signal, which reflect the material information of the loose particle. The specific descriptions are shown in p-value [The research processes of the proposed feature optimization method are as follows. First, statistical methods ,33,34, sp-value  to selecWith the help of the loose particle material identification experimental system, we obtained the initial material dataset. The dataset contains 1,039,776 pieces of data. Among them, there are 175,416 data points with the label “0”, 169,943 data points with the label “1”, 177,936 data points with the label “2”, 168,796 data points with the label “3”, 173,105 data points with the label “4”, and 174,580 data points with the label “5”. It should be noted that labels “0” to “5” represent the six materials in According to complete data in the dataset, five statistical methods, including mean filling, median filling, mode filling, Lagrange interpolation filling, and Newton interpolation filling, were used to process the missing values, and five processed datasets were obtained. In addition, we used the direct-discarding method to discard the whole section of data where the missing values were located, retaining the data without missing values to obtain the sixth dataset.On this basis, we applied the RF classifier to make predictions on the above six datasets. In order to obtain relatively accurate prediction results, the RF classifier made ten predictions on each dataset and obtained ten prediction accuracies. We took the mean value of ten prediction accuracies as the final achieved identification accuracy in order to reduce the random influence. It can be seen from the table that the RF classifier achieved the highest identification accuracy (59.63%) on the material dataset processed by the direct-discarding method. This is the same missing value processing method that we used previously. Meanwhile, the RF classifier achieved a slightly lower identification accuracy on the dataset processed by other statistical methods. Therefore, for the material dataset, the direct-discarding method is the best missing value processing method. This also shows that if the dataset contains a small proportion of missing values, they should be discarded directly without worrying about losing part of the information; this will improve the overall quality of the dataset. After the missing value processing stage, we a material dataset that does not contain missing values. The material dataset contains a total of 1,038,667 valid data points, which can be used for standardization and normalization processing.We further analyzed the numerical distribution of the features within the material dataset and found that the data scales between the features of each column were quite different. For example, the value range of the spectral centroid was 0.9 to 1, whereas the value range of the Cepstral coefficient difference was 400 to 800, and the data scale difference between the two was more than 400 times. Therefore, we standardized and normalized the material dataset, aiming to align the data distribution of the row features or column features within the dataset  and ensuThe main purpose of z-score standardization is to unify the data of different levels into the same level and measure the data by the calculated z-score value so as to ensure its comparability . The meaIn the formula, Min–max standardization replaces the input value with the output result of the following formula :(2)m=norm(It can be seen from Equation (4) that we used the L2 norm . Other tWe used the above three methods to process the material dataset obtained in the missing value processing stage. Similarly, we applied the RF classifier to make ten predictions on the three processed datasets, taking the mean value of the ten classification accuracies so as to indirectly characterize the processing effects of the three methods. It can be seen from the table that the RF classifier achieved the highest average identification accuracy of 63.51% on the dataset processed by min–max standardization, which was significantly improved compared to 59.63% accuracy before processing . In addition, we can also see that the identification accuracies achieved by the RF classifier on datasets processed by z-score standardization and row normalization were also significantly improved, although there are still some gaps compared with the former. Similarly, after standardization and normalization processing, we obtained a material dataset with relatively regular data distribution, which can be used for feature selection.p-value. We performed feature selection on the dataset after min–max standardization processing and analyzed the feature selection effects. Furthermore, we effectively combined the feature selection method based on Pearson correlation coefficient and p-value and proposed a new multi-index-fusion feature selection method. Therefore, the effective features that contribute considerably to the identification accuracy of the RF classifier were selected, and a high-quality material dataset was constructed. The new feature selection method effectively improved the prediction performance of the RF classifier on the material dataset and considerably improved the identification accuracy of the existing loose particle material identification.In machine learning, the prediction performance of the classifier increases with an increase in the number of used features . HoweverPearson correlation is also called product–difference correlation or product–moment correlation. The Pearson correlation coefficient can be used to measure the linear relationship between the features of each column in the material dataset . The gre follows :(5)ρX,Y=In the formula, Pandas to calculate Pearson correlation coefficients between each column feature and the label in the material dataset and draw a heat map, as shown in When using the feature selection method based on Pearson correlation coefficient on the material dataset, we treated labels as a fixed variable and other column features as another variable. Thus, Pearson correlation coefficients between each column feature and the label can be calculated. In this case, the closer the calculated correlation coefficient is to 1 or −1, the more important the column features used for calculation. Values of the calculated correlation coefficient closer to 0 indicate that the column features used for calculation are relatively less important. We used In the heatmap shown in MD), spectral centroid (mainHz), and Cepstral coefficient (MSF). We processed the dataset after min–max standardization processing and reserved only the column data corresponding to the three column features to form a new dataset. The RF classifier was used to make predictions, and the achieved average identification accuracy was 48.76%. Compared with the identification accuracy achieved by the RF classifier on the dataset after min–max standardization processing, the identification accuracy decreased significantly. This is because the feature selection method based on Pearson correlation coefficient selected a small number of features, so a considerable amount of material information contained in the original dataset was lost.Furthermore, by setting the threshold of the absolute value of the Pearson correlation coefficient to 0.1, we selected and retained the three features of energy density  was used to calculate the absolute values of Pearson correlation coefficient between features and labels in the material dataset, which are expressed asIn the formula, Step 2: Based on the obtained absolute values of the Pearson correlation coefficient of each feature and label in the dataset, Step 3: In the above process, we used a single evaluation index (Pearson correlation coefficient) to rank fourteen features. In the next step, we used the second index  to evaluate fourteen features in the same way. Similarly, we calculated the p-value(s) of all features in the dataset and expressed them asp-value) to rank the fourteen features. Finally, it was necessary to conduct a comprehensive analysis based on the two ranking results to achieve the final evaluation of the fourteen features.So far, we used the second index , degree of symmetry between left and right (dczy), pulse rise proportion (Tp), duration (Tl), energy density (MD), pulse ratio (ZB), area ratio (dp), spectral centroid (mainHz), variance (var), Cepstral coefficient (MSF), Cepstral coefficient difference (MSFcha), and Zero crossing rate (zerorate). The highest identification accuracy was 64.46%. The multi-index-fusion feature selection method retained twelve columns of feature data in the material dataset; According to the comprehensive rankings in p-value, and the multi-index-fusion feature selection method. It can be seen from the table that compared with the identification accuracy of 63.51% achieved by the RF classifier on the material dataset containing fourteen features, the RF classifier achieved an identification accuracy of 64.46% on the material dataset that contained twelve features after feature selection. Despite the reduction in two columns of feature data in the material dataset, the identification accuracy achieved by the RF classifier was improved by 0.95%. Similarly, we also found that compared to using a single evaluation index, the multi-index-fusion feature selection method organically combined the two evaluation indices and achieved an average identification accuracy higher than either of them. In other words, the average identification accuracy of the multi-index-fusion feature selection method is higher than that of the traditional feature selection based on the “filtering method”. This shows the superiority of the new feature selection method on the material dataset.The proposed feature optimization method of material identification for loose particles inside sealed relays achieved the highest identification accuracy of 64.46% on the material dataset, which is significantly improved compared with 59.63% obtained in our previous study. We summarized the above-mentioned research process and obtained the general procedures of the feature optimization method of material identification for loose particles inside sealed relays:Step 1: Missing value processing stage. For the missing values in the material dataset, the direct-discarding method was used for processing.Step 2: Standardization and normalization stage. In order to solve the problem of irregular data distribution in the material dataset, the min–max standardization method was used for processing.Step 3: Feature selection stage. In order to improve the prediction performance of the RF classifier, the multi-index-fusion feature selection method was used to retain the features with a large contribution to the prediction performance of the RF classifier.According above description, the overall quality of the processed material dataset was significantly improved compared with that before. This concludes the description of the general procedures of the proposed feature optimization method.In the previous description, we carried out missing value processing, standardization and normalization processing, and feature selection processing for the material dataset and obtained the optimal processing method at each stage. In the missing value processing stage, the direct-discarding method achieved the best performance. In the standardization and normalization stage, the min–max standardization method achieved the best performance. In the feature selection stage, the designed multi-index-fusion feature selection method achieved the best performance. In the “verification and analysis” stage, we prepared new sealed relay samples, used the loose particle material identification experimental system to conduct experiments, and built a material verification dataset. We tested the obtained optimal feature optimization methods of each stage on the material verification dataset, thus proving the robustness of the processed feature optimization method.In order to verify the testing effect of the proposed feature optimization method of material identification for loose particles inside sealed relays in real-world application scenarios, we prepared new sealed relay samples. Following step 7 in the technology implementation process in Step 1: We fixed the sealed relay sample on the PIND experimental platform and applied mechanical excitation to it by driving the vibration table so that the loose particle inside the sealed relay sample was in a collision or sliding state.Step 2: The generated loose particle signal was collected by the acoustic emission sensor provided by the loose particle detection system and sent to the detection system for signal conditioning and synchronization, and the collected signal data were sent to the host computer for storage.Step 3: We processed the signal data and extract fourteen features that considerably contribute to the identification accuracy of the RF classifier from the processed signal data to obtain multiple pieces of data.Step 4: We adjusted the sealed relay sample selected in step 1 and repeated steps 1 to 3 to obtain data representing the new material. By sequentially selecting the sealed relay samples, a material verification dataset representing loose particles of different materials can eventually be built.Step 5: We followed the general procedures in Step 6: We applied the RF classifier to make predictions on the material verification dataset, obtained the predicted label of each data point in the dataset, and completed the prediction of the material of loose particles.Following steps 1 to 4 and through a large number of balanced experiments, we built a material verification dataset representing the loose particles of six materials, the specific descriptions of which are shown in In order to evaluate the effect of the proposed feature optimization method, it is necessary to apply the RF classifier to make predictions on the processed material dataset and indirectly indicate the processing effect of the feature optimization method by measuring the obtained identification accuracy. Therefore, we selected the identification accuracy as the evaluation index to measure the prediction performance of the RF classifier.Suppose that the dataset isa points ,60. The In the formula, Following Step 5 of the verification steps in It can be seen from the table that the RF classifier achieved satisfactory identification accuracy on the material verification dataset processed in each stage, and the achieved identification accuracy in each stage improved significantly compared to the previous stage. This is consistent with the improvement trend of identification accuracy achieved by the RF classifier on the processed material dataset in each stage, which is shown in Following the same verification steps, we once again prepared multiple sealed relay samples and ultimately built ten material verification datasets. We followed step 5 of the verification steps in p-value. It was found that most of the identification accuracies achieved by the RF classifier on the material verification datasets processed by these two methods were decreased, a small part was basically unchanged, and only one was slightly improved. This effectively illustrates the superiority of the multi-index-fusion feature selection method proposed in this paper.It can be seen from the table that the proposed feature optimization method not only achieved an obvious optimization effect on the material dataset but also achieved satisfactory identification accuracies on the ten material verification datasets, which effectively proves the practicability and robustness of the method in engineering applications. It can be concluded that the average improvement of identification accuracy achieved by the RF classifier was 3.01%. In the feature selection stage, we also used the feature selection methods based on Pearson correlation coefficient and (1)From another perspective, we considered how to improve the identification accuracy of existing loose particle material identification methods and transferred our research focus from the selection and optimization of classification algorithms to the optimization of the internal features in the material dataset.(2)Methods in feature engineering were applied to conduct preliminary optimization processing on the material dataset, including missing value processing, standardization, and normalization processing. This effectively ensured a complete dataset and that the numerical distribution of all column features in the dataset had an approximately equal scale.(3)p-value were analyzed. Accordingly, a multi-index-fusion feature selection method was designed. Thus, the features that contribute considerably to the prediction performance of the RF classifier were selected, and a new high-quality material dataset was constructed.The feature selection methods based on the “filtering method” were studied, and the feature selection effects of the feature selection methods based on Pearson correlation coefficient and (4)The feature optimization method proposed in this paper was tested many times on validated datasets. Test results show that the RF classifier achieved the highest identification accuracy on the material dataset that processed by the proposed feature optimization method in this field. It has important reference value to trace the production process of loose particles in the manufacturing process of sealed relays. This method is an important supplement to existing loose particle detection technology and has extremely high application value for improving the reliability of aerospace system. In theory, it can be extended to the design of feature optimization methods for other datasets in machine learning.The problem of loose particles is an important factor that affects the reliable operation of sealed relays. With further research on loose particle detection technology, this problem has attracted increasing attention from scholars. Based on our previous research on material identification, in this paper, we transferred our research focus from the selection and optimization of classification algorithms to feature optimization of the material dataset and designed a complete set of feature optimization methods, including directly discarding missing values, performing min–max standardization processing, and applying the multi-index-fusion feature selection method. The proposed feature optimization method achieved considerable effects on both the material dataset and ten material verification datasets, and its feasibility was verified. The contributions of this paper can be summarized as follows:It should be noted that the material dataset shown in p-value and then ranked according to the cumulative sum. In this process, we treated the evaluation results based on Pearson correlation coefficient and p-value equally. In the future, before directly accumulating the ranking numbers of the two, we will consider introducing the weight coefficient. In other words, we will multiply the two rankings by the weight coefficient and the opposite of the weight coefficient, respectively, and add the weighted rankings; then, a new ranking is carried out according to the new cumulative sum. In this way, by adjusting the weight coefficient, we can set the bias to the evaluation results based on Pearson correlation coefficient or p-value, and to some extent, we can avoid the situation of repeated comprehensive ranking numbers. For example, ifIt is also worth noting that the proposed multi-index-fusion feature selection method directly summed the ranking numbers based on Pearson correlation coefficient and The most considerable innovation presented in this paper is the shift of research focus from the selection and optimization of the classification algorithms to the features within the material dataset. The root of the impact on the prediction performance of the RF classifier lies in the loose particle signal. Therefore, a more complete statement can be made as follows: the collection of high-quality loose particle signals is crucial to the preliminarily constructed material dataset, the material dataset after feature optimization is crucial to the preliminarily trained RF classifier, and the parameter-optimized RF classifier is crucial to the loose particle material identification accuracy. Therefore, in future research, it is necessary to study how to collect high-quality loose particle signals. On the one hand, it is necessary to study the selection and layout of acoustic emission sensors in order to collects loose particle signal with as much energy as possible. On the other hand, it is necessary to study the excitation conditions of loose particles, that is, to set the best matching excitation conditions for loose particles of different materials and different weights.In our early research on loose particle material identification, we focused on the applicable machine learning classification algorithm and parameter optimization. In this paper, we chose to transfer our research focus to the features within the material dataset.First, we compared the processing effects of mean filling, median filling, mode filling, Lagrange interpolation filling, Newton interpolation filling, and the direct-discarding method, which completed the tasks of the missing value processing stage and obtained the optimal direct-discarding method.Second, we compared the processing effects of z-score standardization, min–max standardization, and row normalization, which completed the tasks of the standardization and normalization processing stage, obtaining the optimal min–max standardization processing method.p-value and combined the two to further propose a multi-index-fusion feature selection method, which completed the task of the feature selection stage. The feasibility and superiority of the proposed feature selection method were proven by several tests.Then, we studied the feature selection methods based on Pearson correlation coefficient and Finally, we applied the complete feature optimization route of “directly discarding missing values, performing min–max standardization processing, and applying the multi-index-fusion feature selection method” to the test of ten material verification datasets, which fully verified the practicability and robustness of the proposed feature optimization method. The test results of the material dataset show that the identification accuracy achieved by the RF classifier on the dataset before and after feature optimization was improved from 59.63% to 63.60%. The test results of ten material verification datasets show that the identification accuracy achieved by the RF classifier on the datasets before and after feature optimization was greatly improved, with an average increase in identification accuracy of 3.01%.The test results show that the proposed feature optimization method of material identification for loose particles inside sealed relays has a significant effect on improving the prediction performance of the RF classifier, and the identification accuracies achieved on the dataset before and after optimization were significantly improved. Moreover, the proposed method performed well in multiple verification tests, which clearly proved its practicability and robustness. It is worth noting that the highest identification accuracy achieved by the RF classifier is also the highest identification accuracy achieved by loose particle material identification in the loose particle detection field to date.


The transition of a linear polypeptide chain into a functional, 3-dimensional protein is fraught with dangers, but a successful outcome is critical to all aspects of life. To assist and monitor this process, all cells and organism are endowed with a cadre of molecular chaperones, their co-factors and folding enzymes . The abiGhozlan et al.). Another review focuses on prefoldin, an adapter that conveys clients to TRiC to maintain protein homeostasis in physiological and pathological conditions . A third review summarizes studies revealing the role of generalist chaperones in protecting the proteome of the parasite and in renovating the host proteome to its own benefit (Blatch).As polypeptide chains emerge from the ribosome, they can begin to fold co-translationally, which could inhibit appropriate interactions with more distal sequences needed to reach their native states. Chaperone families including Hsp70, Hsp90 and the cage-like chaperonins are found in all lineages of organisms  and serve to protect reactive regions on nascent proteins. They are particularly non-selective and can recognize a vast number of sequence unrelated proteins and can therefore be considered generalists. Hsp70s recognize linear stretches of amino acids that are hydrophobic in nature and will ultimately be buried upon correct protein folding and/or assembly . They prcis Golgi, which returns it and any bound client to the ER . Equally important to maintaining the secretory proteome is the ability to identify proteins that ultimately fail to mature and/or assemble properly and target them for degradation. In most cases this involves extracting the protein from the ER for degradation by cytosolically localized proteasomes in a process referred to as ER-associated degradation (ERAD) .The endoplasmic reticulum (ER) is the gateway into the secretory pathway and is responsible for the biosynthesis of 1/3 of the proteins encoded in the mammalian genome, which includes proteins that are secreted, expressed on the cell surface, or that populate all organelles of the secretory pathway . Stringeo the ER . A revien (ERAD) . The prePittari et al.).An ER stress-sensing response termed the Unfolded Protein Response (UPR) transcriptionally adjusts chaperone levels to the secretory capacity of the cell . As suchCaenorhabditis elegans . In the other they show that the C. elegans ortholog of the main muscle transcription factor MyoD, is essential to preserve proper expression of the muscle specific chaperone system not only during differentiation but also in adulthood (Nisaa and Ben-Zvi).The chaperone system is extremely plastic. For example, generalist chaperones, like those of the Hsp70 family, can cooperate with different co-chaperones, changing their substrate specificity . Higher Travaglini et al.).Whereas the core proteostasis network consists of the mechanisms driving protein translation, folding and degradation, stress response pathways such as the UPR, the Heat Shock Response (HSR), the Integrated Stress Response (ISR) and the oxidative stress response add levels of regulation that allow the cell to cope with and survive situations that unbalance proteostasis . Cancer


Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain–computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications. Wireless power transfer (WPT) can enable neural implants with lightweight, miniaturized form factors and sustainable operation, which will benefit the study of the long-term development of neural activity and reveal the working mechanism of the brain ,2,3. OptElectromagnetic fields impinging on the human body could lead to the power dissipating in tissue and increasing the temperature. For safety reasons, the Federal Communications Commission (FCC) sets the specific absorption rate (SAR) to less than 1.6 W/kg ,10. The Bioimplants such as brain–computer interfaces (BCI) usually have low power consumptions of several milliwatts, so their impedance is typically in the hundreds or thousands of ohms ,20,21,22Reduction of the secondary side equivalent load resistance variation caused by the primary side input voltage change can stabilize the state of wireless power transfer and reduce energy loss and heat dissipation. By using the LDO to regulate the secondary side output voltage, the equivalent impedance change can be decreased, and the frequency mismatch of the secondary side can be decreased. The output efficiency of LDO can reach 90% under the condition of a low voltage drop, which can further improve the secondary side efficiency of the wireless power transfer system. With the decreased equivalent impedance change, the voltage gain variation can be decreased, which is beneficial for accurate control of the secondary voltage output. Additionally, LDO is easier to integrate on-chip and does not easily cause EMI problems. For the primary side, the class E amplifier circuit is widely used because of its simple structure and high theoretical efficiency . HoweverIn this paper, an LDO-based linear-power-regulated wireless power transfer method is proposed. The transfer function of the wireless power transfer system with different typologies was obtained by using the gyroscope theory, and the factors affecting the voltage gain were analyzed and verified by simulation. A miniaturized linear-power-regulated wireless power transfer system was developed. The wireless power transfer system was tested, the parameters were changed and the influence of the system efficiency was analyzed.Bioimplants are usually implanted with several millimeter-diameter coils; the coupling coefficient between the coils is small so that the optimal load impedance of the wireless power link is usually around a few ohms. To reduce the equivalent load impedance, the primary side in series and the secondary side in parallel (S–P) topology is usually adopted, as shown in We used the gyroscope theory to calculate the transfer function of S–P topology; the load ,33,34. Tthe load ,35. The When the primary side uses a constant voltage source as input, the secondary side can be seen as a constant voltage source output. When the coil parameters are fixed, the output voltage gain is affected by the load resistance a times, the input voltage is Considering the use of an ideal rectifier and voltage regulator, when DC-DC is used to adjust the output voltage, the load can be regarded as a constant power load, thus the equivalent load resistance from the DC-DC input side is Equation , and theEquation .(3)a·V1Substitute Equation  into EquEquation .(5)gs−pAccording to Equations (4) and (5), when the S–P structure has DC-DC for the output voltage regulator, the equivalent load impedance and voltage gain change non-linearly with the primary side voltage and bring two aspects of impact:1. The variation of the secondary side equivalent load resistance makes the parallel equivalent resistance and the primary side reflected resistance change. The parallel equivalent impedance Equation , where RWithout considering the internal resistance of the transmitter and receiver coils, the reflected impedance Equation .(9)ZrefIn the S–P topology, Equation .(10)ZreIt can be seen from Equation  that adj2. The change of the imaginary part causes the off-resonance of the secondary side and reduces efficiency. For parallel resonance on the secondary side, the resonance condition is represented by Equation .(11)jωLWhen the primary side voltage changes, the imaginary part of the parallel equivalent impedance is represented by Equation .(12)jXCAccording to Equation , when thEquation .(13)η2−The efficiency of the secondary side is reduced and the reactive power component is increased, so the transmit voltage needs to be further increased to achieve the power consumption required by the load. The increase of the transmitter voltage increases the SAR and endangers biological safety.The load resistance can be matched through the reactive power matching network, which makes the equivalent load resistance closer to the optimal load impedance ,36. The Similarly, the gyroscope theory was used for analysis, and the S–SP structural transfer function was obtained as shown in Equation .(14)V1IWhen using a constant voltage source input, it is similar to the S–P structure and the output has similar constant voltage source characteristics, and Equation .(15)V2VIt can be seen from the above analysis that the reduction of the load change makes the wireless power transfer link more stable and the change of the reflected impedance is also reduced.For the further reduction of the load resistance change caused by the primary side input voltage change, and to improve the stability and control accuracy of the wireless power transfer, we considered using LDO as the voltage controller on the secondary side as shown in The following is a detailed analysis of its impact on wireless power transfer. Since the excess power will be consumed by the LDO, the power consumed by LDO is Therefore, when LDO is used to adjust the output voltage in the S–SP topology, the resistance change at the secondary side can be represented by Equation .(18)ΔRLWhen DC-DC is used to adjust the output voltage, the resistance change at the secondary side is shown in Equation .(19)ΔRLIt can be seen that Equation  into EquEquation , the volEquation .(20)g′=The voltage gain is changed from nonlinear variation to linear variation when LDO is used. The above analysis results show that the use of LDO to adjust the output voltage can further reduce variation of the resonant frequency, the parallel equivalent impedance and the reflection impedance, which provides the possibility for the primary side to precisely control the secondary side output voltage.We performed simulations using Ansys Electronics Desktop Software with an ideal DC-DC and LDO. The load worked at 3.3 V output, 500 To verify the proposed method of using LDO to realize the parameter of the wireless power transfer system variation linearly, we developed a miniaturized linear-power-regulated wireless power transfer system as shown in The secondary side matched the load impedance by adding a series capacitor, and formed the S–SP wireless power transfer topology together with the primary side. The received energy was rectified using a full-bridge rectifier consisting of four Schottky diodes  with a maximum 0.6 V voltage drop. The rectified voltage was regulated through a low quiescent current 1.8 V output LDO  and connected to a 500 The transmitter coil and receiver coil were simulated by Ansys HFSS. By adjusting the coil size, the coil performance parameters obtained by Ansys HFSS simulation were consistent with those used in wireless power supply simulation. The inductance of the transmitting coil was about 1.5 IC control DC-DC in this system can only output a voltage of 1.8–5.5 V, it can only generate a sine wave signal with a maximum amplitude of 5.5/2 × 4/The test environment is shown in The test results of voltage gain and primary side current are shown in 1. As the primary side voltage increases, the power consumption of the circuit increases, which makes the temperature of the circuit increase, therefore resulting in a decrease in voltage gain;2. When the LDO is used to adjust the output voltage, the current at the secondary side increases synchronously with the primary side voltage, resulting in an increased voltage drop of the rectifier. Make the measured output voltage decrease after rectification.The current at the transmitter is shown on the right axis. As the input voltage changes, the current linearly increases, indicating that the reflected impedance at the transmitter can be kept stable when LDO is used to adjust the output voltage.The overall efficiency of the wireless power transfer system and the partial secondary side efficiency are shown in By analyzing the transfer function of the wireless power transfer system, we determined that the load impedance change is the main factor affecting the state of the wireless power transfer when the primary side voltage is changed. In the existing structure, DC-DC is generally used to adjust the output voltage, so that the wireless power transfer system has the strong robust performance to cope with the possible large changes in the coupling coefficient. However, the equivalent load resistance is a non-linear change with the primary side input voltage, causing the nonlinear variation of the resonant frequency and the reflected impedance. The efficiency of on-chip DC-DC is usually less than 70% under low power conditions; the impact of the resulting heating dissipation in the living body is difficult to measure.When LDO is used to adjust the output voltage, the load impedance change can be from a nonlinear to a linear change and reduces the change amount. When using LDO, it is necessary to strictly control the rectified output voltage to keep the efficiency of the LDO above 90%. Since the change of the reflected impedance is reduced, the change of the coupling coefficient can be calculated and the output voltage can be accurately adjusted. The actual test shows that the efficiency of the secondary side is above 80% when using LDO, and when the rectified output voltage is closer to the LDO output voltage, the efficiency will be further improved. Compared with the use of DC-DC to adjust the output voltage, the use of LDO greatly improves the efficiency of the secondary side, and significantly reduces the heat dissipation in the living body. Moreover, LDO is easier to integrate on-chip, and its small area and low noise features are more conducive to applications such as neural recording. The comparison of on-chip LDO and DC-DC in wireless power transfer is shown in As far as the rectifier loss is concerned, when using DC-DC as a power regulator, the rectifier bridge loss can be reduced by increasing the voltage output. Since the voltage drop caused by the rectifier changes linearly with the flowing current, in the application of implanted devices, due to the low current, the actual test found that the loss caused by the rectifier bridge is extremely low, so it is not particularly important to optimize the efficiency of the rectifier. The selection of ultra-low drop Schottky diodes, semi-active rectifier circuits, or voltage doubler rectifier circuits will further reduce the loss of the rectifier.In recent years, distributed multi-node brain–computer interface implants have emerged ,42. CompWe propose a linear-power-regulated wireless power transfer method for decreasing the heat dissipation of fully implantable microsystems in this paper. With the LDO on the secondary side, the parameters of the wireless power transfer system change linearly in the power control process. An LDO-based linear-power-regulated wireless power transfer system was developed and tested; the secondary side efficiency can reach more than 80% with the primary side voltage control, which is significantly improved compared with the use of DC-DC to regulate the output voltage and can effectively reduce the heat dissipation in the organism. The linear-power-regulated wireless power transfer method can reduce the dependence on wireless communication in optimal efficiency tracking applications and improve biological safety.


The article presents research on the performance of composite and monolithic sensors for distributed fibre optic sensing (DFOS). The introduction summarises the design of the sensors and the theoretical justification for such an approach. Lessons learned during monitoring cracked concrete are summarised to highlight what features of the DFOS tools are the most favourable from the crack analysis point of view. Later, the results from full-size laboratory concrete specimens working in a cracked state were presented and discussed in reference to conventional layered sensing cables. The research aimed to compare monolithic sensors and layered cables embedded in the same reinforced concrete elements, which is the main novelty. The performance of each DFOS nondestructive tool was investigated in the close vicinity of the cracks—both the new ones, opening within the tension zone, and the existing ones, closing within the compression zone. The qualitative (detection) and quantitative (widths estimation) crack analyses were performed and discussed. Finally, the examples of actual applications within concrete structures, including bridges, are presented with some examples of in situ results. During each phase, relevant control procedures should always be specified and applied to ensure optimal decision-making ,2. TheseBecause of the complexity of civil engineering projects, including material and geometrical imperfections, uncertainties, time-depended effects, construction technologies and many others, structural safety cannot be ensured only by theoretical considerations, even taking into account the newest achievements in advanced numerical simulations. The knowledge must be developed based on the analysis of real structures’ behaviour, provided by in situ measurements. This task is currently being handled by structural health monitoring (SHM) systems ,5 operatIt must be emphasised that failure consequences in civil engineering and geotechnical facilities include not only financial, social, and environmental losses but also loss of human health or life. This is the reason for the constant development of new solutions improving structures’ reliability and long-term high-quality performance.Despite the advantages of conventional (discrete) structural health monitoring systems, a direct answer to the question about the state  of the structure between the measurement points is still not possible. The solution to this problem is provided by distributed fibre optic sensing (DFOS), enabling the measurements with spatial resolution so high that they could be treated as geometrically continuous from an engineering point of view. The concept of smart structures with integrated systems based on distributed fibre optic sensors has been intensively developed for several years. Instead of a single number obtained by standard spot gauge, these sensors measure the profiles of strains , displacThe above feature is especially useful regarding concrete because of its heterogeneity, local imperfections, changes in stiffness caused by the random presence of aggregate and dependence on many other factors like mixture components, time (rheology), curing process or load procedure. The most crucial aspect that should be underlined, though, is concrete discontinuities in the form of multiple cracks. They are a common and natural feature of reinforced and prestressed concrete structures . CrackinThe possibility of analysis of the structural behaviour from a real zero stress-strain state .Integration inside the structure (concrete) provides a more accurate transfer of the measured physical quantity from the structure to the sensor .Natural protection of the embedded sensors against mechanical damages, aggressive external environmental or direct sunlight. The expected operation lifetime of such a system should be comparable with the operation lifetime of the structure itself.Comprehensive data gathered by distributed fibre optic sensors, replacing thousands of conventional spot-type sensors, provide a new quality in structural measurements and become the reason why DFOS nowadays is finding a growing acceptance in the laboratory, and above all, in field applications. It is worth underlining that it is possible and favourable to install the DFOS sensors inside the structural components  during cThe general idea of distributed sensing described above can be utilised using different techniques reflected in the data loggers available on the market. Three main optical phenomena used for that purpose are Rayleigh, Brillouin and Raman scatterings, named so to honour their discoverers . It is wRayleigh scattering [attering  is used attering   and polyimide (stiffer). The final choice should be made considering the following relationships:To enable safe handling of the measurement fibre on site, it must be somehow protected. There are two basic concepts utilised in practice. The earliest approach involves techniques known from telecom applications, where a set of intermediate layers are arranged around the fibre for its mechanical protection. In the worldwide literature, these solutions are known as layered sensing cables ,36,37. TThe presence of intermediate layers implicates the risk of mutual slippage between them and thus, influences the correct strain transfer from the concrete to the measuring fibre inside the cable. The problem of slippage is especially noticeable during high strain values and high strain gradients close to the cracks. This phenomenon was observed and described in many works ,39,40,41Another uncertainty is caused by thermal changes, which, in the case of different and unknown thermal expansion coefficients of subsequent layers, may produce unknown strains in the fibre. Thus, the mechanical and physical properties of the cable’s components and their adhesion parameters must be carefully studied before application within the cracked concrete structures to enable the correct data interpretation. Moreover, the name ‘cable’ corresponds to the feature of these tools, i.e., their usability only when subjected to tension loads. If compression strains are expected, the pre-tensioning process should be performed during installation , which iFinally, the type of outer surface of the cable should be considered to provide its best possible bonding between the surrounding concrete. Another concept for fibre protection is proposed by monolithic sensors, which are produced as composite bars in the pultrusion process . The glaAlthough the general idea and operation rule of monolithic sensors remain the same, their properties could be adjustable depending on the specific requirements of the project in question. Both geometrical  and mechanical  parameters can be considered. The design of a DFOS-based system involving monolithic sensors for crack detection in concrete is presented hereafter and discussed based on actual data from laboratory and field tests.The research shows the performance of monolithic sensors when monitoring cracked concrete structures in comparison with selected layered cables. All of these tools are commercially available on the market and widely used, so the presented results would have important meaning for practical applications in the future.qualitative analysis: cracks’ detection and location , changing over time, which could be compared to the thresholds defined in relevant standards to answer the question about the crack’s significance for load capacity or durability. The measured strain profile in the close vicinity of the crack should be converted into the crack width  with reasonable accuracy, useful from a practical point of view (not worse than 0.05 mm). A few procedures are presented in the literature ,48,49,50The main goal of distributed fibre optic sensing for cracked concrete is to enable the advanced analysis of cracks. However, it should be clarified what this analysis means in practice. Two issues should be considered:Distributed sensing through a fibre optic sensor is supported by several optical phenomena already outlined in Today, the above requirements on high spatial resolution are usually met by Rayleigh scattering. For standard Brillouin-based systems, spatial resolutions range from 200 to 1000 mm, which is insufficient to solve the stated measurement problem . However, it should be clearly emphasised that thanks to the dynamic development of DFOS techniques, the performance of optical data loggers  is constantly being improved. Other parameters should be counted when selecting the appropriate one, like accuracy, strain resolution, precision, or repeatability.DFOS tools are a critical component of the entire system. They are fully integrated with the monitored concrete structure  to provide reliable measurements during the predicted period. Preferably, this time is equal to the operational lifetime of the structure itself. In most projects, it is difficult, and sometimes even impossible, to replace the DFOS tools, while the reflectometer can be easily replaced in case of failure.High accuracy ensured by unambiguous strain transfer from the structure to the measuring fibre inside the DFOS tool. This feature is characteristic of a monolithic cross-section of the sensor without any intermediate layers, which disturb measurements by extensive slippage. The lack of slippage within the sensor itself allows for reducing uncertainties and simplifying mathematical models used for strain transfer analysis and crack width calculation;High strain range allowing for undisturbed readings of crack-induced deformations without fear of damaging the sensor’s components. Fast-yielding materials like steel or plastic tend to remember the localised historical strains rather than reflect the actual deformation state of the structure. This is especially dangerous during long-term monitoring while cyclic loads are expected;Rough outer surface of the sensor must provide the best possible bonding with the surrounding concrete, not only through the adhesion but also mechanical clamping (similar to the reinforcing bars). For instance, it could be done by ribs, braids or perforated grooves [ grooves ;Resistance to harsh conditions, which are expected during the construction and operation of civil engineering structures. The design of the sensor must provide appropriate protection against mechanical damages, local transverse forces , alkaline concrete environment and other aggressive factors;High durability provided by appropriate material. As the sensors are usually fully integrated within the concrete, the expected operation lifetime should be equal to the lifetime of the structure itself;cannot require pretension, as this process in construction or geotechnical conditions is challenging or often impossible. Selected stiffness of the sensor’s core must ensure correct positioning without extensive waving, as deviations from the designed position will result in additional errors during data interpretation. Reliable readings must be possible both in the tension and compression zone;DFOS tools Prove the sensors’ high performance in at least tens of engineering projects.The high-quality and long-term performance of DFOS tools dedicated to civil engineering and geotechnical applications must be ensured by meeting the set of the following requirements:Debonding effect between the outer surface of the sensor and the surrounding concrete is natural and cannot be entirely eliminated. Perfect bonding means the full compliance of concrete and sensors strains, but in case of concrete discontinuity (crack), it would result in the sensor’s breakage. Thus, debonding over the section called “effective length” appears, reducing the infinite theoretical strain to the level which will not cause the sensor’s breakage. This is how the reinforcement bars work in the close vicinity of the crack a. DesignExample parameters of two different monolithic strain sensors are summarised in Monolithic sensors can replace the composite reinforcing bars and be included in strength calculations of the structure itself. On the other hand, to increase the sensitivity in crack detection and to avoid strengthening the structural elements, it is favourable to reduce the axial stiffness as much as possible . Figure After selecting the DFOS technique and type of monolithic sensor, the next important issue influencing the system’s reliability and efficiency is the installation method. It depends on the type of structure  and the mounting time .the maximum bonding surface between the sensor and the surrounding concrete ;natural protection against mechanical damages;significant reduction of thermal influence (direct sunlight) on strain results;the beast aesthetics without mounting elements visible on the surface.The common approach for existing structures is to glue the sensors to the prepared  concrete surface ,52. The The adhesive parameters, similar to the sensors themselves, should be selected carefully to ensure the best strain transfer mechanism. Too stiff adhesive can crack itself, while too soft can underestimate the actual structural strains.Sensors can be installed not only within the concrete surface but also on the reinforcing bars along their longitudinal rib b. Based The most convenient way of installation is applied in the new, reinforced structures. The sensors cut at the required length are usually delivered on site in coils, and the only task is to unroll them and stabilise them in designed positions by tying to the existing reinforcement using cable ties c. The siε(x) is the strain measured over the distance x, ΔL(x) is the mechanical strain change and ΔT(x) is the temperature change.The measured strain profiles using Rayleigh or Brillouin scattering are affected both by mechanical loads and thermal changes. In most general terms, the following equation can be presented:Providing that temperature changes between the subsequent readings are constant, measured strains are directly equal to the mechanical strains , i.e., generating the stress inside the structural component. This situation is possible only in laboratory conditions or during short-term field measurements, especially overnight or within underground installations.However, during long-term monitoring, knowledge of thermal changes is unconditionally necessary to enable the correct engineering interpretation of structural behaviour. Appropriate correction can be applied based on the technical specifications of monolithic sensors and the guidelines of their producers. Usually, in the standard operating range (from −20 °C to +60 °C), it is enough to use a single (linear) coefficient, including the sensor core’s thermal expansion and the refractive index’s temperature-dependence of the optical fibre itself. This task became more complicated and uncertain with coated fibres  and layeUsing the strain DFOS sensors with a Raman-based optical datalogger, which is insensitive to mechanical loads and thus, allows only for temperature measurements; DTS (distributed temperature sensing). This solution is primarily dedicated to long distances (km order).Using the strain sensors with both Rayleigh-based and Brillouin-based optical datalogger. These two techniques are depended on mechanical strains and temperatures to varying degrees. Knowing the individual coefficients for each technique, it is possible to solve a system of equations in which the unknowns are mechanical strains and temperatures . It is wUsing special DFOS temperature sensors and one from DFOS techniques for strain measurements (Rayleigh or Brillouin). The idea behind this solution is to isolate the measuring optical fibre from mechanical strains, for instance, by placing it inside the tube. The fibre is then, apparently, subjected only to temperature changes. However, the free fibre does not exist because of the friction between the tube and the fibre. It can cause disturbances in the measured temperature profiles, especially considering longer distances. In addition, high mechanical strains expected while monitoring the cracks in concrete can consume excess fibre inside the tube.Using conventional spot temperature gauges and approximating the temperature field between the measurement points. That approach is justified when no high gradients over length are expected .Nevertheless, the profiles of temperature changes between the subsequent readings over the entire monitored length are the basis for further compensation procedures. Today, there are four possible approaches to obtaining them:σε) and those related to the change in length (Lε)—actual shortenings or elongations consist of the stress-free part  and part generating the stress. For correct crack width estimation, strains related to the real change in length should be considered. For example, for ultimate limit state analysis, only strains generating the stress are essential.Multiple aspects must be considered when designing the DFOS system for crack monitoring in concrete structures. After selecting the DFOS technique, appropriate sensors, installation methods and thermal compensation approach, the last thing is to elaborate algorithms and procedures for data post-processing, allowing the system’s main objectives to be achieved. First, raw strain data should be validated, and eventual distortions should be removed. For the inexperienced user, cross-correlation failed points can be misinterpreted as cracks . Then, tAs described in The proposed step-by-step procedure of designing a DFOS system dedicated to crack monitoring in concrete structures, with general descriptions, is summarised in C1: sensing cable with three plastic layers .M1: monolithic, reinforcing sensor M2: monolithic, flexible sensor C2: sensing cable with two plastic layers and steel insert .The laboratory tests described hereafter were one of the first in the world, allowing for direct comparison between different types of DFOS tools embedded in the same reinforced concrete beams. A total of six beams with rectangle cross-section 250 × 350 mm were made, with tools installed in the lower (tension) and upper (compression) zones. The spatial visualisation of the single specimen is presented in To avoid damage or changing the position of the tools during concreting, self-compacting concrete was applied c. There Another implemented approach was to use a concrete mixture with a large amount of Portland cement and to leave the top surface of the beams after concreting without any curing a. The idThe research was performed in the Building Materials and Structures Research Laboratory at Cracow University of Technology, Poland. Beams were loaded in four-point bending tests using the universal machine with the max. load cell capacity of 1000 kN, which is permanent equipment of the laboratory a. The maThe DFOS readings were done statically step-by-step (while increasing force) using a Rayleigh-based datalogger with an optical switch connected to all embedded tools inside the beam b. DuringThe test course has been established separately for S- and L-type beams due to their different structural response resulting from different reinforcement . Due to the many beams, load steps and multiple cracks formed during research, in the following part of the article, the entire procedure and results will be presented and discussed for the example beam S3 for the sake of transparency. In the beginning, the experiment was steered (controlled) by the displacement of a hydraulic cylinder, which was stopped at selected force values. All the beams were loaded up to the structural failure, which was achieved by losing the bending capacity in S-type beams and shearing capacity in L-type beams.A detailed comparative analysis of all DFOS tools in crack detection and width estimation will be the subject of another article . This paThe graphs presented in Despite the minor differences in the shape of strain profiles, which could be caused by the random course of the cracks and their varying size over the beam’s width, it should be stated that analysed tools could detect all the existing cracks correctly. The locations of strain peaks were in line with the external observations. Moreover, the crack width changes were calculated by strain integration over the effective length, and the maximum difference between the tools during subsequent load steps did not exceed the value of 0.01 mm, while the mean difference was a few times smaller. Such a value, treated as an error in width estimation, allows for correct structural safety assessment and can be usually neglected from an engineering point of view.Although the results provided by all the analysed DFOS tools in the compression zone were satisfying, it should be underlined that the obtained strain range was minimal in comparison to the declared maximum strains . Moreover, while analysing the behaviour of reinforced concrete structures, the tension zone is of the main interest, including cracks increasing their widths.The above observation about a significant decrease in the crack-induced strain peaks in the tension zone is valid for all the beams and all the cracks. Because the strain data gained through distributed sensing are expressed in two domains: structural length and time (load step), they could be conveniently presented in the form of spatial visualisations. The example plots corresponding to data in The locations of all the cracks were identified unequivocally by a monolithic sensor with an accuracy equal to applied spatial resolution (10 mm), which was confirmed by reference readings using a metric tape on the side surface of the beam and photo-camera. On the other hand, the indications from the layered cable are not conclusive, especially given the negligible increments during cyclic loading. In real projects, very often, the zero reading is taken after the formation of the cracks and width changes are usually much smaller than in laboratory conditions (under the applied force). Thus, the risk of data misinterpretation is too high. The next step of the analysis was focused on the crack width estimation, which was done by strain integration over the effective length. This length was assumed to be equal to half of the mean spacing between the multiple cracks (the conservative assumption about overlapping the effective lengths). The local crack-induced strain peaks indicated the centre of this length. The crack widths calculated for the example load step no. 26 are summarised in Considering the above specific example, but also global statistics presented in , the mosAnother advantage is the highest strain range. The maximum value of tensile strain registered in the last load step, just before the structural failure, was equal to 2.86% , while the strain range declared by the producer is ±40,000 µε. The corresponding crack width exceeded 1.3 mm at that time. The ability to measure extensive cracks without fear of damaging the sensor is an essential aspect while monitoring cracked reinforced concrete structures.Although the layered cable with steel insert inside provided reasonable results in the compression zone in the very limited strain range (<550 µε), its performance was unacceptable within the tension zone. The registered crack-induced strain peaks were almost four times lower compared to the monolithic sensor, and the shape of strain profiles did not allow for certain crack detection. The maximum declared value of correct strain measurements  is not feasible. It is worth noticing that the steel layer inside the cable yields itself at the strain level of approx. 0.2%. The main reason for unreliable readings, though, is disturbed strain transfer from the concrete to the measuring fibre caused by the internal slippage between the intermediate layers in the cable.Considering the above findings, the layered tools are not recommended for high-spatial-resolution DFOS measurements of reinforced concrete structures when crack detection and analysis are of the main interest.Field proofs are the last and most important step in verifying the effectiveness of DFOS-based monolithic sensors. This section describes three examples of actual concrete structures equipped with such sensors, giving the example results related to crack analysis. The first is a new bridge with sensors embedded inside the concrete slab. The second refers to the existing 10-years-old bridge, where the near-to-surface installation method was applied. Finally, the very old concrete sewer collector is described as an example of an ageing infrastructure requiring the appropriate control and maintenance.Three monolithic sensors were used to monitor the performance of the concrete slab in a railway bridge north of Frankfurt am Main, Germany. Since the decision to create the system was made before construction was completed, it was possible to embed the sensors inside the slab. The installation consisted of stabilising the sensors in their designed positions by tying them to the existing reinforcement along the main bars .Sensors with appropriate lengths were delivered on site in coils, and the ease of installation allowed it to be completed within half a day. As the sensors were installed before concreting a, the stHowever, the measurement performed the next day, 20 h after concreting is completed, clearly indicates the presence of the first ten cracks (red line on the plot). Their spacings and locations depend on the geometry of the structure and boundary conditions. The estimated crack widths did not exceed the value of 0.06 mm.Cracking in concrete structures is a normal process taking place from the first days, and it usually cannot be avoided. The structural engineer’s main task is to monitor and control the crack positions and widths over time. Distributed fibre optic sensing allows all the cracks to be detected and analysed, even inside the structural elements, where visual inspection is impossible. However, a precondition for the system efficiency is the use of high-definition monolithic sensors, high-spatial-resolution DFOS technique and appropriate data processing algorithms.The DFOS-based system in the Frankfurt bridge was used to detect the first cracks formed during early-age concrete behaviour  but also to monitor structural response according to the planned schedule, including long-term static measurements as well as dynamic readings during the train passages.Although embedding sensors inside the components of new structures is most beneficial from the quality and quantity of information obtained, it is also necessary to be aware of the existing, ageing infrastructure . The folDue to the limited width of the route below the lower crossbeam, the pylon’s legs are inclined to minimise the size of the foundation . The pylon’s legs have a rectangular cross-section with variable dimensions: 6.0 × 7.0 m at the foundation level, 4.0 × 4.0 m near the upper crossbeam and 4.0 × 6.0 at the top.Although the decks are suspended by the set of cables, they are also (to a lesser extent) supported by the prestressed lower crossbeam of the pylon. However, the main task of this prestressed element, with the midspan rectangle cross-section of 2.5 m (height) and 4.0 m (width), is to connect two inclined legs of the pylon, which, due to their geometry, tend to move outwards b.After ten years of the bridge’s operation, multiple cracks (with widths up to 0.1 mm) were observed over the length of this crossbeam, which is a natural consequence of its static scheme and loading. However, due to the unfavourable environment above the river, controlling cracks widths changes over time (according to the standard’s requirements) is very important.The Rędziński Bridge was equipped with DFOS monolithic sensors in 2020, joining the honourable group of several bridges in Poland with this innovative monitoring solution. Four sensors were glued inside the pre-made near-to-surface grooves a locatedThe cyclic measurement sessions are now performed according to a planned schedule, with reference taken shortly after the installation. In a single reading, the strain data from 7400 locations are recorded, providing direct crack detection and estimation of their width changes (both while closing and opening).Next to the long-term analysis, the short-term readings  were also done under the random traffic loads. The width changes identified then did not exceed thousandths of a millimetre. Example results from the lower and upper sensor are presented in Despite such small values of crack widths changes, the system was able for a reliable diagnosis thanks to the appropriate strain transfer from the concrete to the optical fibre fully integrated inside the monolithic core of the high-sensitivity sensors.Four spot temperature gauges were installed within the crossbeam to compensate for the long-term thermal influences. It is also worth noticing that the applied strain sensors can be used simultaneously with other dataloggers  to measure different physical quantities like distributed temperatures or vibrations. Finally, thanks to the known arrangement of the sensors within the beam’s cross-section, it is possible to calculate horizontal and vertical displacements , as well as axial shortenings or elongations.In 2022, the force adjustment within eight shortest stays was performed, relieving the pylon’s lower crossbeam and bending it up slightly. Distributed fibre optic monolithic sensors were used for the advanced and simultaneous analysis of the beam’s strains, cracks and displacements.3/s, while the recorded capacity may be higher during heavy rainfalls.The last example refers to the Burakowski sewage collector, an essential element of the sewage system in Warsaw, Poland . It was The unfavourable external conditions in the close vicinity of the collector include the edge of the high bank of the Vistula River, as well as subway and tram lines and stations. In addition, several tall and deep-founded buildings have been built in this area over the last 15 years. In 2015, the construction of a new “Burakowski Bis” collector was completed passing along the existing one, which resulted in environmental changes (including ground-water conditions) compared to the times when it was constructed.After analysing the collector’s technical condition in 2019, a decision was made to retrofit a 4.8 km long section using non-circular 3 m-long GRP module panels and relining technology. The space between the panels placed inside and the existing inner walls of the concrete collector was filled with a cement injection. Thus, the possibility of a visual inspection of the collector was lost.This was one of the reasons for equipping the old concrete part with a DFOS-based system. Three monolithic sensors were installed longitudinal over the length of the 146 m-long section between the maintenance holes. One top sensor and two side sensors a,b in thWhat is more, one monolithic strain sensor was installed along the circumference of the collector’s cross-section a, creatibefore GRP modules were provided inside the collector (zero reading),after the GRP modules were placed (before grout injection),after grout (mortar) injection,and finally, after the collector was put back into service and filled with sewage.Strain measurements were taken during the retrofitting and strengthening works, including the following stages:DFOS readings allowed observing the collector’s structural response under gradual load changes.The dead weight of the new panels and mortar injection, as well as the dead weight of the launched sewage flow, caused the original structure of the concrete collector casing was reflected in DFOS readings. Increasing mechanical load during the renovation work revealed cracks caused by technological breaks, and changes in their widths were carefully analysed during this process. The application of the DFOS system based on monolithic strain sensors allowed for detection of all discontinuities in the collector structure, most of which were undetectable by the naked eye during visual inspection. The system is now used to control the crack widths over time during periodical measurement sessions, supporting the expert in assessing the technical condition and structural safety of the collector.There has been a noticeable increase in distributed fibre optic sensing deployment in recent years, not only in laboratory research but, above all, in real civil engineering and geotechnical projects. The main advantage is the possibility of continuously measuring selected physical quantities over length to fulfil one of the fundamental goals for structural health monitoring: direct damage detection and estimation of its size. However, to utilise the advantages of DFOS approach, a set of requirements must be understood and implemented to obtain high-quality and reliable data.The versatility of this technique lies in the ability to customise system elements  according to the individual needs of a given project. In reference to reinforced concrete structures, it is essential to be aware that they operate in a cracked state and that a reliable assessment of local discontinuities (cracks) of a very small size  is one of the primary challenges for the DFOS system.The lessons learned while monitoring cracked concrete structures were summarized in the article to highlight what features of the DFOS tools are the most favourable from the crack analysis point of view. During the selection, not only their internal design should be considered, but also the strain range and resolution, axial stiffness, cross-section area, elasticity modulus, bending radius, material and its resistance to corrosion, durability, no need for pre-tensioning, the tendency to wave and more.Theoretical considerations were supported by laboratory tests on full-size components. The novelty of the research lies in direct comparison between specific monolithic sensors and layered cables commercially available on the market, and often used for the same purposes. The obtained results have, thus, practical meaning, showing significant differences in the performance of different DFOS tools.The findings include that layer-free sensors are adequate solution for crack analysis. The measuring fibre does not slip inside the cross-section, as it does in layered cables, providing that the structural strains are transferred in an as undisturbed way as possible. Based on the performed measurements, a reliable interpretation of the deformation state of the structure was possible.The DFOS-based monitoring of cracked concrete structures is a challenging process. In addition to the monolithic sensors, which are one of the key components of the entire system, the appropriate high-spatial-resolution technique should be selected , as well as installation methods which directly influence the physical interpretation of the obtained data. This article is a part of a discussion about the best DFOS configuration, which could be recommended for application in cracked concrete, depending on whether we are dealing with new or existing structures. A set of good practices based on lessons learned while applying monolithic DFOS strain sensors were presented in this article and discussed through laboratory and field proofs.