Due to its potential impact on business efficiency, automated customer complaint labeling and classification are of great importance for management decision making and business applications. The majority of the current research on automated labeling uses large and well-balanced datasets. However, customer complaint labels are hierarchical in structure, with many labels at the lowest hierarchy level. Relying on lower-level labels leads to small and imbalanced samples, thus rendering the current automatic labeling practices inapplicable to customer complaints. This article proposes an automatic labeling model incorporating the BERT and word2vec methods. The model is validated on electric utility customer complaint data. Within the model, the BERT method serves to obtain shallow text tags. Furthermore, text enhancement is used to mitigate the problem of imbalanced samples that emerge when the number of labels is large. Finally, the word2vec model is utilized for deep text analysis. Experiments demonstrate the proposed model's efficiency in automating customer complaint labeling. Consequently, the proposed model supports enterprises in improving their service quality while simultaneously reducing labor costs. As quality of life improves, customers have higher expectations for products purchased and services received. Major enterprises strive to remain aligned with customer interests by creating customer complaint channels that resolve customer disputes and dissatisfaction. In addition, with an increase in business competition, customer churn reduction becomes increasingly important. The critical aspect of reducing customer churn is customer satisfaction improvement. Thus, customer complaints, which reflect customer satisfaction, form an essential bridge between customers and companies.Although the number of complaints is often large, enterprises commonly rely on manual methods for processing complicated complaint content. As a result, customer complaint processing requires significant labor and time. Since text mining enables automatic analysis of customer complaints, it can reduce labor costs, promote customer satisfaction, and prevent customer churn. Thus, research on text mining of customer complaints can play a crucial role in improving enterprises' efficiency.Automatic labeling and classification refer to the use of computational procedures that generate tags to summarize, describe, and classify textual content. Current research on automatic labeling of both short and long texts mainly relies on keyword extraction and topic modeling. However, the derived labels do not have a hierarchical structure. Customer complaint texts, on the other hand, are processed in accordance with the complaint classification, where the responsible customer service department is contacted, customer complaints are processed, and the processing results are returned. Based on the level of admissibility, complaint texts are usually divided into different business categories that can be visualized with a tree-like structure. For example, Fig. Therefore, the customer complaint labels are characterized by a hierarchical structure but also the excessive use of deep labels and a relatively small, imbalanced sample size. When a traditional autolabeling algorithm assigns a text to the lowest admissible business category, the text can be understood to automatically belong to the relevant higher admissible business category. However, such an approach does not make effective use of hierarchical information between categories. Further, customer complaints can have numerous deep labels, resulting in small and imbalanced samples. Such samples pose an obstacle in training an autolabeling model.1. Word2vec is a technique used to calculate word vectors2. Based on a given corpus and a training model, word2vec quickly and efficiently represents a word in the form of a vector that enables the calculation of the word-to-word similarity.This paper addresses the described issues by proposing a BERT and word2vec-based automated customer complaint labeling model. The bidirectional encoder representations from transformers (BERT) method is a pretrained language representation model and is viewed as landmark work in the field of natural language processing (NLP). BERT makes full use of a large number of unsupervised texts for self-supervised learning and encodes linguistic knowledge. Experiments demonstrate its superior performance on various NLP tasksAn automatic customer complaint text indexing model based on BERT and word2vec is proposed.A text enhancement method is proposed to improve the problem of imbalanced text and improve the accuracy of the automatic indexing model.In the process of matching the text with the indexing label, layer-by-layer semantic matching is adopted. First, the shallow label belonging to the text is determined, and then the deep label under the shallow label is used for text matching, rather than directly matching the text with the deep label, which significantly improves the accuracy of the automatic indexing model.The model presented in this paper enables the hierarchical classification of customer complaints. Within this work, class labels at the second hierarchical level  are called shallow labels. In contrast, class labels for Level 3 admissible business classes are called deep labels. The model's automatic labeling of customer complaints is divided into two stages. In the first stage, the BERT classification algorithm is used to identify the shallow labels. In the second stage, the deep labels are determined by calculating the similarity between the text and labels to be matched. The class label with the highest similarity to the text is selected as the complaint label. The BERT-based classification method is used for shallow text labels, and a similarity calculation model based on word2vec is used for deep text labels. The contributions of this paper are as follows:3. More precisely, automatic labeling is the process of extracting words and phrases directly from the original text to describe the document's subject matter.As noted in “4 introduced computer technology into bibliographic studies and devised a word-frequency method based on Zipf's law. Earl5 combined syntactic analysis and statistical word-frequency methods to extract keywords. The work of Salton et al.6 applied the vector space model for autolabeling, whereas Deerwester et al.7 suggested the use of latent semantic analysis. Finally, Anjewierden and Kabel8 proposed an ontology-based method for automatic labeling. Hugo9 proposed an innovative method to address the complexity of events in medical event logs. Based on automatic labeling, similar events are clustered in potential space to create accurate labels. Su10 proposed an automatic evaluation and labeling architecture for product perceptual attributes based on a convolutional neural network.Automatic indexing research began in 1957 when LuhnImbalanced text is characterized by a significantly smaller number of elements in one category than in other categories. An important problem facing natural language processing and machine learning at this stage remains the efficient processing of imbalanced text in classification tasks. Major NLP tasks, including sentiment analysis, propaganda detection and event extraction from social media, are all examples of imbalanced classification problems.11 proposed a hybrid imbalanced data learning framework (HIDLF) to address the imbalance of views in a movie review dataset and then classified the movie reviews by the proposed HIDLT-SVM algorithm. Harish12 proposed using a BERT model to address the problem of data imbalance in text classification. Li13 proposed a solution to the imbalanced text problem in a multiclassification task. The multiclass dataset is first decomposed into several binary datasets. Then, spectral clustering is used to divide a minority of the subset of binary categories into subspaces and oversamples them according to the characteristics of the data. Spectral clustering-based sampling takes into account the distribution of the data and effectively avoids oversampling outliers. After the data have reached an approximate equilibrium point, a multiclass classifier can be trained from these rebalanced data.AdinarayanaFigure Data acquisition: Through consultation and cooperation with the power company, customer complaint data are obtained, which includes the customer complaint text and the corresponding tag.Among them, the label is divided into a first-level label, a second-level label and a third-level label. For example, a customer submitted the following complaint: “The customer asked a staff member to deal with the power failure at home, but the staff member did not deal with it and did not explain the reason to the customer.” The text belongs to the "service attitude of other personnel" (third-level label) under "service behavior" (secondary label) under "service complaint" (first-level label).Data cleaning: Data cleaning mainly includes text filtering and hierarchical label set extraction.Text filtering mainly manually filters the customer complaint text. Due to the freedom of customer complaints, there are some meaningless noisy data, so it is necessary to rely on manual screening of the obtained data.The extraction of the hierarchical label set organizes the indexing labels of the text data to form a hierarchical label set.Text enhancement: The (too) deep labels lead to a few samples in several categories. Thus, the classification process commonly deals with imbalanced samples that affect classification accuracy. Within this work, this problem is addressed by enhancing the texts falling in critical categories.Data preprocessing is a series of normative operations performed on the acquired irregular and chaotic texts. The goal of preprocessing is to reduce irregularities, remove inaccuracies, and reduce noise. This step serves as a basis for the subsequent text manipulation. Within this work, data preprocessing consists of the following steps:Oversampling is the process of generating a bias towards selecting the data from specific categories. In other words, it is a data enhancement technique that increases the likelihood of choosing a positive case, thus overcoming the issues stemming from the imbalance of positive and negative samples. However, the adjusted dataset will often be much larger than the original dataset, increasing the time overhead.The main methods of data enhancement in NLP are adding noise and paraphrasing. Noise addition modifies a positive case by, for example, randomly deleting certain words or disrupting the word order to generate new data. Paraphrasing can be seen as a seq2seq task. For example, in question-and-answer systems, question retelling is used to improve the question format. Then, the new question is used instead of the old question in the question-and-answer system.Image pixels commonly contain noise, which often does not have a significant impact. However, in this study, a small change in a text may greatly impact the task since the deleted words are likely important. Removing an unimportant word, on the other hand, is equivalent to deleting a stop word. Disrupting the order of many word is even more infeasible, as sentence disruptions remove the contextual relationship among words. Thus, text generation is seen as a more effective way to enhance the texts within this work.In this paper, data enhancement is divided into two steps. The first step extracts the texts from underrepresented categories and translates them. The texts are translated from Chinese to English and back. The newly obtained Chinese texts contain different expressions and can be saved as new texts under the category tag. This procedure doubles the number of complaints in the category. The second step—synonym substitution—has to be done to increase the number of texts even further.The customer paid 400 RMB at the State Grid Pengshan Power Supply Office this morning.The customer refunded 400 RMB at the State Grid Pengshan Power Supply Office before noon.The customer repaid 400 RMB at the State Grid Pingshan Power Supply Office half a day before.The second step uses the Harbin Institute of Technology (HIT) thesaurus and the partitioning tool in the NLP package pyltp. First, the thesaurus is traversed, and a synonym list is constructed for each word in the sentence. For example, if a sentence contains the word "pay", the list of synonyms include "compensate, give, refund, repay, pay off…". One restriction is introduced. Namely, synonym replacement is not performed for very short words since they are commonly adverbs or prepositions , for which synonym replacement is not very meaningful. Next, the three top-ranking synonyms are selected as a final result for each word in the synonym list. If a word has less than three synonyms , the exact word is copied three times . In this manner, each word in the sentence is associated with three words, and a simultaneous replacement of multiple words in the sentence generates the new sentence. Since the thesaurus is not comprehensive and short words are not replaced, the original sentence parts are retained, while the other parts are replaced with synonyms. For example, the following three instances are obtained from a single sentence:Following the described synonym substitution procedure, each original sentence can be expanded into three sentences. If additional data are required, more sentences can be generated by utilizing longer synonym lists. Substitution by synonyms introduces a level of randomness in the sentences, forcing the model to learn deeper levels of semantic information and, consequently, enhancing the model robustness and reducing the influencing data factors. Using the HIT thesaurus has at least two advantages. First, using the thesaurus eliminates the need to calculate similarity and thus avoids the inaccuracies in calculating the word2vec similarity caused by scarce customer complaints data. Second, the HIT thesaurus does not distinguish between specific domains. Several words in the electricity field may not have synonyms, such as "State Grid Pengshan Power Supply Business Hall". Using thesaurus-based synonym replacement can, to a certain extent, alleviate the emergence of fluency problems.Text is unstructured data. To rely on a computer for text classification, the text must be transformed into computer-comprehensible structured data. Therefore, text representation is crucial for natural language processing tasks. Traditional word vector representation models include the TF-IDF model and word2vec neural network language model. The TF-IDF model performs vector representation of the text based on the product of the term frequency (TF) and inverse document frequency (IDF); word2vec maps words to a low-dimension and high-density vector space by training a neural network model. However, these text representation models have limitations: they do not consider the meaning of words in context. Taking the word “book” as an example, in the sentence “I am reading a good book on economics”, “book” means a written work or composition that has been published. In the sentence “the agent booked tickets to the show for the whole family”, “book” means arrange for and reserve (something for someone else) in advance. A traditional word vector used to represent the model cannot effectively combine context and distinguish semantics, which is obviously unreasonable. To solve this problem, this paper uses the BERT model for text representation.In recent years, several studies have successfully mitigated the listed problems by pretraining deep neural network models and fine-tuning them to perform specific NLP tasks. BERT is one such pretrained language model with output vectors, a word-level vector, and a sentence-level vector. Sentence-level vectors can capture the entire sentence's semantics and are often used in classification tasks.In this paper, a BERT-based text classification model is proposed. Every BERT sequence starts with a special classification token denoted by CLS. The model first takes the CLS vector representing the sentence, and then the sequence is passed to a fully connected layer. The model uses a binary cross-entropy loss function and the softmax function, which is an activation function typically used in multiclassification tasks.C is the input vector at the sentence level, The specific structure of the model is shown in Fig. N is the total number of class labels and To enable text classification using the model, the class labels must first be encoded and transformed. The class labels of each sample are mapped to a list Once the text is represented using BERT, the CLS vector is extracted and passed to the full link layer. The softmax function normalizes the output nodes' values so that the sum of the output nodes equals 1. The softmax function is defined as:After using text enhancement to solve the problem of category imbalance, this paper uses a BERT-based text classification model to classify customer complaint texts and uses the classification label as the text indexing label of the text at the shallow level.The next step is to find the deep label under the shallow label based on the shallow label obtained by the BERT-based text classification model and the level-level label set obtained by data cleaning and use the text semantics based on word2vec similarity matching to determine the deep tags.At present, there are many studies on question similarity matching, which can be roughly divided into question similarity research based on strings, question similarity research based on vector space models and question similarity research based on deep learning. The focus of this paper is not to propose a highly accurate or innovative question similarity matching model but to propose a set suitable for this problem and effective solutions, so this paper uses the commonly used question similarity calculation method: based on word2vec text vector representation, the cosine similarity of the calculation vector is the text semantic similarity.n-dimensional vector, but—upon training—the models obtain an optimal vector for each word.Word2vec uses two models, the continuous bag-of-words (CBOW) model and the continuous skip-gram model. The goal of the CBOW model is to predict the probability of a word based on the current context, while the skip-gram model does the opposite and determines the probability of a context based on the presented word Fig. . Both moWord2vec trains both CBOW and skip-gram models on a given corpus, and the output yields vector representations of all the words that appear in the corpus. The obtained word vectors enable the calculation of word-word relationships, such as word similarity and semantic relatedness.This paper uses training set text data to train the word2vec text vector representation model. Through the shallow label obtained by the previous text classification model based on BERT and the hierarchical label set obtained by data cleaning, the deep label under the shallow label is found. Word2vec creates a vector representation of the index text and deep label. By calculating the cosine similarity between vectors, the deep label to be matched with the highest similarity is taken as the deep label of the text.The last phase of the autolabeling process deals with determining the final label from shallow and deep text labels. At the shallow level, the label is validated using the text-enhanced BERT classification model. Similarly, at the deep level, the validation process relies on word2vec to calculate the semantic similarity between the text and the potential labels. The final text label is determined by combining the shallow and deep citation labels.A power company provided anonymized experimental data to protect the privacy of its customers. The experimental data consist of the electricity complaint text and the primary, secondary, and tertiary business categories to which the text belongs. The experiments reported in this paper were performed on the first- and second-level categories. The data were cleaned to ensure the accuracy of the experiment. After the initial manual screening and the removal of noisy data, a total of 16 shallow labels corresponding to 3438 complaints were selected as the experimental dataset. The distribution of the experimental dataset is shown in Table In the data preprocessing stage, to improve the BERT classification effect, this paper uses the text enhancement method to expand samples with too little text under the indexing label. There are two main text enhancement methods used in this paper. The first method is to first translate the Chinese text into English and then translate the English text back to Chinese to obtain text with the same meaning but different expressions. The second method is to replace synonyms, using the word segmentation tool in HIT pyltp and a thesaurus to replace some words in a sentence with their synonyms to expand the corpus. After text enhancement, there are 3698 text data points, and the data distribution is shown in Table As described, the BERT-based text classification algorithm is first used to determine the shallow label for every complaint. The data were divided into training and test sets at a 9:1 ratio, yielding a test set of 369 complaints. Within the BERT classification model, the initial learning rate was set to 0.00001. Furthermore, the Adam optimizer was utilized, and the number of epochs was set to 2. For deep labels, the word2vec model was trained on an enhanced dataset with a dimensionality set to 100, and the resulting word2vec model was used for semantic similarity matching.The accuracy denotes the probability that the classifier correctly classifies a sample. In other words, it is the ratio of the number of correctly classified samples to the total number of samples:The precision is the ratio of true positives to the number of samples the classifier assigned to the class:The recall is the ratio of true positives to the number of truly positive samples. In other words,The F1 score is the harmonic mean of P and R and is calculated as follows:The model's performance was evaluated using the accuracy, precision, recall, and F1 score measures. The measures are defined below; TP denotes "true positives" , and TN stands for "true negatives" . FP and FN stand for "false positives" and "false negatives", respectively, i.e., the number of complaints falsely classified as either belonging or not belonging to a class.The shallow labeling results are shown in Table We also choose to use the LR classification models of BERT without text enhancement and TF-IDF with text enhancement to conduct experiments. The performance results are shown in Table The table shows that the text-enhanced BERT model outperforms both the BERT model (without enhancement) and the TF-IDF-LR model with respect to every measure. The TF-IDF-LR model had lower overall accuracy, recall, and F1 score than the BERT model applied to the nonenhanced dataset. The results for each category are shown in Table Table The deep labeling results are shown in Table Using the method of semantic matching layer by layer and similarity calculation to automatically label the customer complaint text, the results are shown in Table This work tackles the problems emerging from the hierarchical structure of customer complaints, where too many deep labels result in small category sizes and imbalanced samples. This paper proposes a new model based on BERT and word2vec that enables the automatic labeling of customer complaints. This model uses text enhancement to mitigate the problem of small category sizes without changing the semantics. In accordance, the model improves the sample size balance.The developed model relies on the BERT model to determine shallow labels and uses the word2vec model to derive deep labels, thus taking full advantage of the hierarchical characteristics of customer complaint labels.The innovations of this paper are as follows. (1) An automatic indexing model of customer complaint text based on BERT and word2vec is proposed. (2) Before determining the shallow text indexing label based on BERT, we first process the text within a category with a very small sample size. By text enhancement, we can obtain more samples and retain the semantics of the samples to improve the problem of insufficient model training caused by the small sample size and class imbalance. (3) First, the shallow label is determined, and then the deep label under the shallow label is used for similarity matching, which makes full use of the hierarchical structure between labels and greatly improves the accuracy.The experiments demonstrated the model's feasibility and validity. Nevertheless, there are several limitations to this work. The first limitation is the issue of data availability since, due to the unique nature of the customer complaint data, the data can be obtained only through cooperation with a power company. Furthermore, since the data are not publicly available, the data quality cannot be determined. Finally, the quality of the enhanced text can be improved by advancing the developed algorithm. Future work will be directed at optimizing the details and improving the model to advance autolabeling.


Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved. Gearboxes are used to transmit torque and power, and are widely used in industrial machinery. Among the various types of gearboxes, planetary gearboxes have the advantages of a large transmission ratio, strong bearing capacity, stable operation, and compact structure. They are widely used in industries or fields of operations such as wind turbines, aviation, ships, and mining. However, due to their frequent operation under the severe conditions of low speed and heavy load, planetary gearbox components wear and break from time to time. In addition, due to the speed fluctuations of the operating conditions, the dynamic signal of the planetary gearbox presents clear non-stationarity. Therefore, monitoring and diagnosing the planetary gearbox are extremely difficult. For example, one wind turbine gearbox in Denmark suffered a serious crack failure during operation, but its monitoring and diagnosis system failed to detect the failure in time, causing serious economic loss . StudiesAt present, vibration analysis is the main method for extracting the state features of a planetary gearbox. To achieve feature extraction of the vibration signal of a planetary gearbox, various signal processing methods have been used, such as envelope analysis , stochasThe vibration signal method suffers from sensitivity to measurement point location selection and complex spectrum law, and so scholars have used other types of signals to describe the state characteristics of the planetary gearbox, such as acoustic emission . BecauseMotor current signature analysis (MCSA) was proposed by Cardoso and Saraiva  in 1991 Traditional current analysis methods are usually used for single-channel current analysis. Three-phase motors are widely used in industrial equipment, and three-phase current signals can provide more information than single-phase signals. The most common method of fusion analysis of the three-phase current–phase relationship is the Park vector method . The basThe traditional monitoring and diagnosis methods are mostly based on signal feature extraction performed under uniform speed conditions. However, planetary gearboxes in industrial equipment often work under variable speed conditions . Order aThe basic idea of tacholess order analysis technology is to extract the characteristics of the analysis signal and calculate the corresponding key-phasor signal. At present, mainstream tacholess order analysis methods mostly take the vibration signal as the object and extract the speed ridge as the instantaneous rotation speed signal through a specific image recognition algorithm to complete the order analysis. Wang Yi et al.  estimateFor state feature extraction under variable speed conditions, traditional time domain and frequency domain analysis methods are no longer applicable. In recent years, with the rise of artificial intelligence and machine learning, research related to these issues has gradually increased, such as that focusing on neural networks and support vector machines ,28,29. TAiming at the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under tacholess conditions, this paper proposes a three-phase current variable-speed tacholess envelope order analysis method. This method completes the instantaneous rotation speed estimation via the three-phase current signals. The order analysis of the envelope signal is completed through angular domain resampling. By extracting the features of the envelope order signal, the linear discriminant analysis technique is used to fuse multiple indicators to form a comprehensive reflection of the characteristics of planetary gearbox operating conditions. Through simulation analysis and experimental verification, the proposed method for evaluating the operating state of a planetary gearbox under variable speed conditions is proven to be effective.The rest of this article is structured as follows. In The instantaneous phase of the current signal under variable speed conditions is no longer linear, meaning the current signal will display clear non-stationarity. At this time, traditional stationary signal analysis methods are no longer applicable, and order analysis is the best method for variable speed signal analysis. This method can overcome frequency leakage, ensure the validity of historical data, and extract power frequency components .Tacholess signal analysis methods usually take vibration signals as their object, extract and estimate instantaneous rotation speed through the time–frequency ridgelines, and complete order analysis . The sigThe instantaneous speed estimation is the most critical part. A significant problem of existing tacholess signal analysis methods is that they struggle to accurately estimate the instantaneous speed. First of all, the main energy of the vibration signals of gearboxes, bearings, and other mechanical parts is not concentrated in the instantaneous frequency. The time–frequency ridges are usually weak and difficult to identify. Secondly, the speed signal usually contains other types of ridges and noise, which greatly affects speed ridge identification.It can be seen from the above analysis that the effectiveness of tacholess order analysis depends on the signal quality and the accuracy of the instantaneous speed estimation. Since the energy of the motor current signal is concentrated on the fundamental frequency, it is less affected by noise interference. There is a phase relationship between the three-phase current signals, which provides very rich frequency conversion information. The above characteristics of the current signal accommodate the requirements of tacholess signal analysis.Based on the above-described basic ideas, this paper proposes a three-phase current tacholess order index extraction method to extract the features from the operating state of the planetary gearbox under variable speed conditions. The basic procedure is shown in The main process of this method is as follows. Since all the torsional vibration information of the current signal is modulated around the fundamental frequency, the Park vector demodulation analysis method is used to obtain the instantaneous amplitude and instantaneous frequency of the three-phase current signals. For speed estimation under tacholess conditions, the proposed three-phase current instantaneous speed estimation algorithm calculates the instantaneous speed corresponding to the current acquisition time. Secondly, the instantaneous speed signal is used for angular domain re-sampling to obtain the instantaneous demodulated amplitude and instantaneous frequency signals. Then, the extraction of order signals can be completed. Finally, following the feature extraction of order signals and the envelope order comprehensive index extraction method, the characteristic information of the planetary gearbox operating state under variable speed conditions can be obtained.The following sections will introduce the principles and algorithms used in each step of the above-described method.In tacholess signal analysis, instantaneous speed estimation is important. For a transmission system, the instantaneous speed of the planetary gearbox can be calculated based on the motor speed and the transmission ratio. Therefore, only the instantaneous speed of the motor needs to be calculated. Under variable speed conditions, the fundamental frequency of the current signal has time-varying characteristics. For an asynchronous motor, assuming that The motor slip rate s is assumed to be constant in this manuscript if the motor is operating under relatively stable conditions.U phase, V phase and W phase current, respectively; It can be seen from Equation (1) that the key to instantaneous speed estimation is to calculate the instantaneous fundamental frequency  gearbox , the thrUsing the Park vector demodulation analysis method , the curThe instantaneous amplitude and instantaneous phase are:The instantaneous frequency (the derivative of the instantaneous phase) is:Ideally, the instantaneous fundamental frequency of the current signal would be the same as the instantaneous frequency, as shown in Equation (6). Due to the presence of electromechanical coupling sites, instantaneous frequency signals will also have frequency components that reflect the characteristics of torsional vibration in actual electromechanical systems. Therefore, the instantaneous frequency of the current signal can be regarded as the superposition of the instantaneous fundamental frequency and torsional frequency components.Generally, the frequency of the torsional vibration signal in a variable-speed mechanical system is much greater than the change frequency of the instantaneous speed, so the instantaneous fundamental frequency appears as a low-frequency trend item in the instantaneous frequency. The problem of instantaneous speed estimation reduces to the extraction of the trend term of the current instantaneous frequency signal. Under the signal processing method, there are many ways to extract trend items. Obvious trends can be identified by function fitting, periodic trends can be obtained through spectral analysis, and implicit trends must be processed by empirical mode decomposition, wavelet packet decomposition, or time series analysis. According to the characteristic frequency of the current signal and the frequency of the rotational speed fluctuation, low-pass filtering can be used to extract the low-frequency signal as the instantaneous synchronous rotation frequency.In summary, the three-phase current instantaneous rotation speed estimation algorithm proposed in this section is shown in Since the instantaneous phase and the instantaneous frequency display a differential relationship, estimation of the instantaneous rotation angle of the shaft requires the instantaneous phase, which is derived from the Park vector demodulation method in the algorithm process shown in Equations (4)–(6). After subsequent operations, the instantaneous angle of rotation can be estimated.After the instantaneous speed is estimated, the analysis of non-stationary signals can be completed by order analysis. In this section, according to the characteristics of the current signal, the traditional order analysis method will be improved, and the envelope order analysis method of the three-phase current signals will be obtained.Order analysis is a method of transforming the time domain signal into the angular domain. Because the fault characteristic frequency of the current signal is modulated around the signal, it is necessary to demodulate the current signal before performing order analysis. The Park vector demodulation analysis method  is used There are two significant differences between the envelope order analysis method employed in this paper and the traditional order analysis method. Firstly, the order analysis in this paper is based on the current envelope signal, and the acquisition of the envelope signal depends on the three-phase current analysis method. Secondly, the traditional instantaneous rotation speed estimation method must calculate the instantaneous phase of the shaft through instantaneous frequency integration, but the instantaneous rotation speed extraction algorithm proposed in this paper can calculate the instantaneous phase while demodulating the three-phase current, avoiding repeated integral calculation.According to the basic theoretical characteristics of order analysis, the envelope order signal calculated by the above process is equivalent to the variable speed as calculated by the steady process. For the analysis of envelope order signals, stationary signal analysis methods, such as spectrum analysis, can be used. Regarding the extraction of the state features of the planetary gearbox under uniform speed conditions, theoretical analysis and experiments show that the entropy and kurtosis coefficient of the demodulated signal can be used as indicators to reflect the characteristics of the planetary gearbox’s operating state . Therefoi-th feature; i-th index to the comprehensive index.Under constant speed conditions, each feature extracted can be used to reflect the operating state of the planetary gearbox. However, under variable speed conditions, due to the influence of speed fluctuations, a single index is not sufficient to summarize all the motion characteristics of the planetary gearbox. Therefore, it is necessary to use certain feature dimensionality reduction and fusion methods to fuse multiple indicator features and thus obtain representative comprehensive indicators. In the field of machine learning, commonly used feature dimensionality reduction methods include principal component analysis (PCA) and linear discriminant analysis (LDA). In contrast, PCA does not require prior label information, and dimensionality reduction and classification are based on the variance of the feature data set, while LDA can use prior label information to focus on the mean difference between features in different classes. In contrast, LDA can obtain more accurate results because of its prior label information. Therefore, this paper chooses LDA as the feature dimensionality reduction method, as it can provide a linear combination of the extracted feature indicators, namely:i-th class after dimensionality reduction; The principle of the LDA algorithm is to find the best coefficient combination ω. The above formula needs transformation to solve the problem. The mean vector of the two types of samples isi-th class.The right side of Equation (8) is not an explicit function of the normal vector For the description of the closeness between samples of the same class, the intra-sample divergence is introduced:For the description of the dispersion of samples between Class 1 and Class 2, the inter-sample divergence is introduced:Equations (9)–(11) are incorporated into Equation (8), which gives:FJ(ω), the partial derivative of ω should be obtained:In order to find the maximum value of The partial derivative is set to 0 and the stationary point is found, that is:FJ(ω) = λ, then the above problem is transformed into the following generalized eigenvalue problem:If this gives ω can be obtained.Therefore, the above problem can be transformed into an eigenvalue decomposition problem. By solving the eigenvector, the The basic flow of the LDA algorithm is shown in To extract the comprehensive indicators, it is first necessary to train the sample model with prior information to obtain the formula for calculating the comprehensive indicators. The labeled sample signals are collected under two typical conditions: normal and abnormal. The sampling conditions of the sample signals can be changed in any order within the predicted speed fluctuation range of the planetary gearbox. The instantaneous frequency and instantaneous amplitude and feature extraction are calculated by the tacholess envelope order analysis method. The extracted features and label information are put into the LDA model for training, and finally, a comprehensive index calculation formula can be obtained. The process of model training is shown in Through model training, the weighting coefficient This section will verify the effectiveness of the proposed algorithm through data simulation analysis. In the variable speed current signal model of Equation (2), we take a meshing frequency order It can be seen from In the time–frequency figures, the characteristics of the instantaneous speed changing with time can be observed. In In addition, from the comparison of The results of the proposed method in this manuscript are shown in From the above simulation results, it can be seen that the three-phase current signal under variable speed tacholess working conditions is non-stationary, and the frequency spectrum appears as a “frequency blur”, meaning effective analysis cannot be completed. Although time–frequency analysis can identify the non-stationarity of the signal and qualitatively determine the changing trend of the speed, it is restricted by time–frequency resolution, and cannot complete an effective instantaneous speed calculation. Using the three-phase current envelope order analysis method proposed in this paper, the instantaneous rotation speed can be estimated by the current signal, and the operating status of the planetary gearbox can be identified through order analysis. In This section will verify the effectiveness of the instantaneous speed estimation algorithm applied to the three-phase current signal. An experiment on the speed change in the planetary gear platform was performed. The experimental process was as follows.The experimental setup is shown in First, the planetary gearbox speed is set and held at The frequency spectrum shown in Since the current signal appears asymmetrically in the frequency spectrum of The current signal appears non-stationary in the time–frequency diagram, as shown in As such, the three-phase current instantaneous rotation speed estimation algorithm proposed in this paper is applied to the above signals, and the instantaneous rotation frequency is obtained, as shown in The instantaneous rotation frequency visualization in In this section, a single-stage planetary gear test bench will be used for a planetary gearbox variable speed fault diagnosis experiment, in order to verify the three-phase current signal tacholess envelope order analysis method proposed in this research.The experimental setup is shown in In the experiment, normal and faulty planetary gearboxes are operated under variable speed conditions, and the current signals of the three-phase motor are collected. The tacholess envelope order analysis method is used to derive the envelope order spectrum, and fault judgment is determined via the difference of the envelope order spectrum.In the experiment, to control the variable-speed operation of the planetary gearbox, the output frequency of the inverter is continuously adjusted at intervals of 0.1 Hz to change the output speed of the motor. Due to the randomness of inverter adjustment, the experiment reflects a general variable-speed process. Correspondingly, the three-phase current signals have the general characteristics of a non-stationary signal.Through structural analysis of the planetary gearbox, the order characteristic frequency of the planetary gearbox is calculated, as shown in The first 5 s of the three-phase current waveform collected from the normal planetary gearbox is shown in The instantaneous rotation speeds of the three-phase current signals of the normal and faulty planetary gearboxes are estimated. The extracted instantaneous phase and instantaneous rotation speed signals are shown in The envelope order spectra of normal and faulty planetary gearboxes are shown in The envelope order amplitude of the normal gearbox only contains the meshing frequency order (In summary, the tacholess order analysis method proposed in this paper can be applied in the state feature extraction of planetary gearboxes under variable speed conditions. Sidebands appear on the envelope order signal under faulty conditions, making the spectrum structure near the meshing order more complicated. To this end, we use index fusion technology to calculate the comprehensive index reflecting the operating state of the planetary gearbox.In this section, we will verify the effectiveness of the algorithm for extracting the comprehensive index of the envelope order via the operation state evaluation experiment of the variable-speed planetary gearbox. The experimental setup described in Tacholess envelope order analysis is performed on the collected three-phase current signals. The time domain entropy of the angular domain’s instantaneous amplitude, the time domain entropy of the angular domain’s instantaneous frequency, the spectral entropy of the angular domain’s instantaneous amplitude, the spectral entropy of the angular domain’s instantaneous frequency, the time domain kurtosis coefficient of the angular domain’s instantaneous amplitude, the time domain kurtosis coefficient of the angular domain’s instantaneous frequency, the spectral kurtosis coefficient of the angular domain’s instantaneous amplitude, and the spectral kurtosis coefficient of the angular domain’s instantaneous frequency are taken as the characteristic values of each group of current signals. A feature vector composed of eight state features can be obtained. The sample characteristics of 100 experiments are shown in There are certain differences in the envelope order characteristics of normal and faulty planetary gearboxes, as shown in From the 50 sets of normal sample data, 40 are randomly selected, and the same is done for the faulty samples. Then, training samples composed of 80 datasets are obtained, and the remaining 20 sets are used as test samples. The mean value of the training set is calculated, then this value is subtracted from the training set, and the training index set is used to build the LDA model. Since the label contains only two categories, the eight features will be reduced to one feature after dimensionality reduction, and this gives the comprehensive index sought in this article. The mapping vector obtained from this experimental data training process is:The elements in the mapping vector represent the weights of the eight features in the comprehensive index. The result of dimensionality reduction for the training set is shown in The comprehensive index shown in According to the above model, a comprehensive index calculation is performed on the remaining test samples, and the result obtained is shown in In In this article, a three-phase current tacholess envelope order analysis method is proposed to solve the problem of feature extraction for a planetary gearbox under variable speed conditions. Firstly, the tacholess instantaneous rotation speed is estimated using three-phase current signals. Secondly, the order of the demodulated current signal is analyzed. Then, feature extraction is carried out for the envelope order signal. Finally, multiple features are fused into a comprehensive index using linear discriminant analysis to complete state feature extraction for the variable-speed planetary gearbox. The simulation analysis and experimental verification results show that the tacholess rotation speed estimation method proposed in this manuscript is effective in extracting instantaneous speed and performing fault diagnosis. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method proposed in this manuscript can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order proposed in this manuscript permits planetary gearbox state identification, and a 95% classification accuracy rate is achieved.


In December 2017, the 15-member ECOWAS (Economic Community of West African States) and the 8-member WAEMU  passed new Tobacco Tax Directives. Both Directives increased the minimum ad valorem excise tax rate to 50%. In addition the ECOWAS Directive introduced a minimum specific tax (US$ 0.02/stick), but the WAEMU Directive did not. This paper examines the likely effects of these new Directives on cigarette prices, sales volumes and revenues.Tax simulation models using comparable data were constructed for each of the 15 countries to estimate the effects of the ECOWAS and WAEMU Directives.If the 15 ECOWAS members implement the ECOWAS Directive it would substantially increase the retail price of cigarettes , decrease sales volumes  and increase tax revenue . The impact of the WAEMU Directive on WAEMU countries’ cigarette prices (unweighted average +2%), sales volumes (−1%) and revenue (+17%) is likely to be minimal.The 2017 ECOWAS Directive, which adds a specific excise tax per pack, along with an increase in the ad valorem tax, substantially improves its members’ cigarette tax structure. The specific tax overcomes the weakness of the ad valorem excise tax, since it does not depend on import or ex-factory values, which comprise only a small part of the retail price in ECOWAS countries. We recommend that WAEMU countries adopt the ECOWAS Directive, rather than the WAEMU Directive. Taxes are the single most effective measure to reduce tobacco use.ECOWAS member states have been even slower than the rest of the sub-Saharan African (AFRO) region to use tobacco taxes as an effective tobacco-control tool . Of thesWhile global smoking prevalence has fallen by more than 10 percentage points since 2000, the number of smokers in Africa is still growing, although from a relatively low base.The two economic blocs in Western Africa are the Economic Community of West African States (ECOWAS) and the West African Economic and Monetary Union (WAEMU). ECOWAS is a customs union, consisting of 15 countries . In customs unions, goods traded within the union are not subject to import duties, and a common import duty is imposed on non-member country imports. WAEMU is both a customs and monetary union, consisting of eight of the ECOWAS countries . Member states share a common currency, and, therefore, monetary policy. Both ECOWAS and WAEMU adopted new tobacco tax directives in December 2017.The new ECOWAS Tobacco Tax Directive substantially increases the ad valorem excise tax rates and addresses systemic problems in the previous Directive.The new WAEMU Directive increases the minimum ad valorem tax rate from 15% to 50%. However, it retains a maximum ad valorem tax rate, which is increased from 45% to 150%.Since WAEMU forms a subset of ECOWAS, the WAEMU member states are, in principle, subject to two substantially different directives. WAEMU countries have mostly complied with the WAEMU Directive. However, due to a lack of institutions and mechanisms to enforce compliance, some countries within ECOWAS and WAEMU have not complied with tax rates set in previous tobacco tax directives.This paper examines how the two directives will affect cigarette taxes, tax revenues, prices and the volume of sales (hereafter the ‘variables of interest’), should all member states implement them. The first set of simulations indicates the price and market changes for each of the 15 ECOWAS countries following tax changes made in accordance with the ECOWAS Directive. A second set considers the impact on the eight WAEMU member states if they were to implement the new WAEMU Directive minimum ad valorem rate of 50%. This is followed by a third set of simulations, should the WAEMU countries increase the ad valorem excise tax to the maximum rate of 150%.The simulation model was programmed in Excel and contains individual models for each of the 15 ECOWAS countries, which include differences in tax rates and structures, as well as different market segments. Each country model is similar to other tobacco simulation models, such as TaXSimThe base model for each of the 15 countries incorporates tax rate and structure information for 2018, or the most recent available information. The first step decomposes the price per pack into its various tax and net-of-tax components. The base-year excise, value-added tax, customs duty and other tax rates  are applIn the second step, taxes are adjusted to comply with the new Directives. For scenario 1, we apply the new minimum taxes from the ECOWAS Directive  for the 15 ECOWAS countries. Scenarios 2A and 2B apply the new WAEMU minimum  and maximum  taxes on the eight WAEMU countries, respectively. Where the existing excise tax meets or exceeds the ad valorem minimums, as in Ghana, Liberia and Senegal, we assume that these countries will not change the ad valorem tax component, and will only introduce the specific component (for the ECOWAS Directive). The Gambia currently has a specific tax that is slightly higher than the new minimum; this also remains unchanged, and the new 50% minimum ad valorem is added. Based on these new taxes, and assuming that all other tax rates and the import, or ex-factory and net-of-tax values remain unchanged, the model calculates new retail prices for each of these scenarios. These new, higher prices decrease the quantity of sales.The impact of the higher prices on cigarette sales is quantified by means of the price elasticity of demand. We use a price elasticity of demand of −0.7 for all countries . Globally, most estimates of the elasticity of demand for cigarettes are in the range of −0.4 to −0.8.Based on the simulated quantity consumed, the new taxes are used to calculate new excise and total tax revenue. The model calculates the new excise tax burden, by dividing the new excise tax amount by the new retail sales price.Given the difficulties with data availability and consistency, we focus on the percentage changes in the variables of interest, rather than the absolute values. If, for example, sales volumes are substantially under-reported in the base scenario, the simulated sales and revenue numbers, in absolute terms, would be biassed downwards, but the percentage change in these variables is not affected, as the under-reporting applies to both the base scenario and the simulated scenario.The main sources of data for the models used in this analysis are the WHO Report on the Global Tobacco Epidemic, 2019 ,Retail prices of the most-sold brand (mostly for 2018) are from the 2019 GTCR. These prices are used as a proxy for the average retail sales price for the nine countries with data for the entire market only as well as for the mid-level segment for the other six countries. GTCR prices for premium and economy brands are used for those market segments, respectively. For Burkina Faso, Cabo Verde, Nigeria and Senegal, imported cigarettes are premium brands. The retail price of the most-sold brand is used for both imports and domestic production in Cote d’Ivoire, since nearly all cigarettes were sold at the same price. Nigeria uses GTCR premium prices for both imported and domestic premium cigarettes.Comtrade data were used for import quantities for all countries. Data are available for most countries for 2017, and for 2018 for Cabo Verde, Ghana and Nigeria . The mosIn all cases, except Cabo Verde, the import value (CIF) is derived from Comtrade data (see Imports from countries within ECOWAS (and therefore WAEMU) are free from duties. We use the sum of reported imports from non-ECOWAS countries from Comtrade to calculate the proportion of cigarette imports that are subject to customs duties. Although customs unions are meant to impose uniform external tariffs, in practice those in ECOWAS countries differCigarettesare not produced in 10 of the 15 countries, so imports constitute total supply. For the countries that both import and produce cigarettes , there would be a substantially greater impact on all variables of interest . Prices The assumption that only the tax increase changes retail prices implies that cigarette producers do not change net-of-tax prices to influence demand and/or to protect their profits after a tax increase. In fact, the tobacco industry can change any of the non-tax components, which affects prices and sales volumes. In We also performed sensitivity analysis using a higher (−0.9) and a lower (−0.5) price elasticity of demand than the model’s value of −0.7  in the ECOWAS Directive has a much greater impact on prices and sales volumes than the increase in the ad valorem rate to 50% (or even to 150%) required by the WAEMU Directive. Implementation of the ECOWAS Directive increases the average unweighted excise tax burden from an average of 13% to 37%. This would be a major change and would make the excise tax burden in the ECOWAS countries higher than the 28% average in the WHO African region in 2018. However, tax shares would still be lower than the global average of 45% .Increasing taxes for the eight WAEMU countries to a minimum of 50% would have a very small effect (Simulation 2A). In three of the eight WAEMU countries, the ad valorem rate at the outset was 50% or more; for those countries the new WAEMU minimum will simply maintain the status quo.Increasing the ad valorem tax component to WAEMU maximum of 150% of import or ex-factory values clearly shows the limitations of increasing the tax rate in a purely ad valorem tax system. It would bring the unweighted average excise tax burden to the 2018 average of 28% for sub-Saharan Africa, compared with 37% should the ECOWAS Directive be adopted.The declared import or ex-factory values in the ECOWAS countries are very low, with a median share of the retail price of about 22% . Even suThe biggest limitation is the lack of reliable country data. The use of Comtrade and WHO GTCR data ensures a level of consistency, but relies on reporting from countries. The validity of the data depends on a country’s data management.Another limitation is that one of the basic assumptions of the model is that there is no change in existing illicit trade levels. The increases in revenues and decreases in sales volumes may be overestimated if smokers switch to cheaper cigarettes. If consumption shifts to illegal cigarettes or other tobacco products, both revenues and legal sales would decrease.Implementation of the 2017 ECOWAS Directive in 2021 would improve the tax structure of these countries by adding a specific excise tax along with the increase in the ad valorem tax. Because the US$0.40 per pack specific tax does not depend on import or ex-factory values, it will overcome the major weakness of the ad valorem tax in the previous Directive.Many countries, in Africa and elsewhere, use the import or ex-factory price as the ad valorem tax base, probably due to constraints on valuing and collecting excise taxes at the retail level because of large informal sectors. However, it is difficult to increase ad valorem rates sufficiently to have a substantial impact on prices, and therefore on sales volumes, from such a low base. A specific tax has the additional advantage of being easier to collect, since it depends on quantities rather than values. It is also be less susceptible to manipulation as it does not depend on the valuation of imports or production.We recommend that ECOWAS member states implement the new Directive in January 2021 in order to improve health, to pre-empt increases in smoking prevalence resulting from aggressive marketing to their young, growing populations, and to increase revenues. WAEMU member countries should opt to comply with the ECOWAS rather than the WAEMU Directive.Tobacco taxes are lower in Economic Community of West African States (ECOWAS) (including the West African Economic and Monetary Union (WAEMU)) countries than the global average as well as the average for sub-Saharan Africa.In December 2017 both ECOWAS and WAEMU adopted new Directives on tobacco taxation. What has been missing is an analysis of the impact on price, sales volumes  and revenues of the two Directives on each of these two blocs’ member countries.This paper constructs cigarette tax models for the 15 ECOWAS countries using each country’s tax rates and structures. We analyse the effects of implementing the two Directives on prices, sales volumes and tax revenues. The paper highlights the weakness of a purely ad valorem system that uses the import or ex-factory values as a tax base, and the advantage of a specific tax that is independent of the value of the product.


The composite material textile reinforced concrete (TRC) requires a high bond performance between the fiber strand and the concrete matrix. While the influence of the textile on bond behavior is well known, in this publication the influence of the concrete matrix is investigated by means of single-sided pull-out tests. The results of the presented study show dependence between the concrete strength and bond performance of the composite material. When a concrete of a higher-strength class is used, the bond-flow–pull-out distance curve shifts upward independent of the textile geometry and the yarn impregnation. A simplified model is presented to predict the occurrence of a crack along the fiber strand. This model serves as a basis to investigate the correlation between concrete width per fiber strand and resistance against a splitting crack. The effective concrete tensile strength decreases to about 35% when the concrete width is increased from 10 mm to 50 mm. To quantify the decrease, a mathematical relationship, which describes the test results independent of textile and concrete type, is proposed. The composite material textile reinforced concrete (TRC), which is composed of a textile reinforcement and a concrete matrix with a small maximum grain size, is increasingly establishing itself in the construction industry. The use of TRC makes sense both in new construction and in repair work ,2,3,4. AIn addition to the high tensile strength of carbon fibers, the geometry of textile reinforcement combined with a stiff impregnation can lead to high bond performance. Thus, fine crack patterns and short anchorage lengths can be realized. However, large bond forces also place greater stress on the surrounding concrete and thus increase the risk of cracking along the fiber strand see . If the 0), a bond flow T = F/L0 (N/mm) can be calculated  related to the bond length of the fiber strand  of the fiber strand is pulled out of the concrete and the pull-out distance is measured. The result is a bond-flow–pull-out distance (crack opening) curve. This is necessary to calculate a bond-stress–slip relationship 0) are asp) is constant for all tests. Regarding the bond-flow–pull-out way-curves a higher bond flow is connected to a greater pull-out way. The greater the pull-out distance, the more the fiber strand is compressed. The question arises if the compression of the fiber strand influences the splitting tendency factor.The model to predict a cracking along the fiber strand presented here is supported by the assumption that the splitting tendency factor  . A smallThe results of the presented investigations show dependence between the concrete strength and the bond performance of textile reinforced concrete. A higher-strength concrete leads to a shift upward of the bond-flow–pull-out distance-curve independent of the textile geometry and the impregnating agent. However, the influence of the concrete is considerably smaller than the influence of different textile configurations. The higher bond forces between the textile and higher-strength concrete also result in a greater risk of splitting cracks.It is shown that a shift of the failure mode from pullout failure in the lower strength concrete to splitting failure in the higher-strength concrete is possible. This can be explained by the greater brittleness of the higher-strength concrete. A comparison of different textiles in combination with two concretes indicates that the splitting tendency tends to decrease with increasing bond strength, regardless of whether the concrete or textile parameter is varied.Testing three strands of yarn compared to one strand of yarn only has an insignificant influence on the failure load concerning a splitting failure in a single-sided pull-out test with short anchorage lengths (one mesh width). This leads to the conclusion that the test on a single fiber strand is thus representative of several fiber strands lying next to each other.When increasing the resistance against splitting failure by increasing the mesh width between the fiber strands, the reduction in the effective concrete tensile strength must be considered. The concrete tensile strength decreases to about 35% when the concrete width is increased from 13 mm to 60 mm. This can be attributed to the stresses in the concrete, which are reduced with increasing distance from the fiber strand. The mechanical model that is presented here includes the factor γ, which transforms the stresses in the concrete to a constant stress block. A polynomial relationship of the second degree is proposed for the determination of this reduction factor in dependency of the mesh width, which describes the test results independent of textile and concrete type. Besides, the correlation can be used to estimate the relative splitting tendency of textiles with different mesh widths.The question arises of whether the results can be transferred to a varying concrete cover. The concrete cover influences the resistance against a longitudinal crack. Therefore, it has to be considered whether the single-sided pull-out test is suitable. The restoring moments in the specimens presented here disproportionately counteract a longitudinal crack failure.Additionally, a verification of the transferability of the results to fiber strands with a larger yarn cross-section  and stiffer impregnation materials should be done. Presumably, a scaling factor with respect to fiber strand width and yarn cross-sectional area is required here.


This How-I-Do-It article presents a modified Deloyers procedure by mean of the case of a 67-year-old female with adenocarcinoma extending for a long segment and involving the splenic flexure and proximal descending colon who underwent a laparoscopic left extended hemicolectomy (LELC) with derotation of the right colon and primary colorectal anastomosis.While laparoscopic extended right colectomy is a well-established procedure, LELC is rarely used . LELC presents several technical challenges which are demonstrated in this How-I-Do-It article.Firstly, the steps needed to mobilize the left colon and procure a safe approach to the splenic flexure are described, especially when a tumor is closely related to it. This is achieved by mobilization and resection of the descending colon, while maintaining a complete mesocolic excision to the level of the duodenojejunal ligament for the inferior mesenteric vein and flush to the aorta for the inferior mesenteric artery. Subsequently, we depict the adjuvant steps required to enable a primary anastomosis by trying to mobilize the transverse colon and release as much of the mesocolic attachments at the splenic flexure area. Finally, we present the rare instance when a laparoscopic derotation of the ascending colon is required to provide a tension-free anastomosis. The resection is completed by delivery of the fully derotated ascending colon and hepatic flexure through a suprapubic mini-Pfannenstiel incision. The primary colorectal anastomosis is subsequently fashioned in a tension-free way and provides for a quick postoperative recovery of the patient.This modified Deloyers procedure preserves the middle colic since the fully mobilized mesocolon allows for a tension-free anastomosis while maintaining better blood supply to the mobilized stump. Also, by eliminating the need for a mesenteric window and the transposition of the caecum, we allow the small bowel to rest over the anastomosis and the mobilized transverse colon and reduce the possibility of an internal herniation of the small bowel into the mesentery.Laparoscopic derotation of the right colon and a partial, modified Deloyers procedure preserving the middle colic vessels are feasible techniques in experienced hands to provide primary anastomosis after LELC with improved functional outcome. Nevertheless, it is important to consider anatomical aspects of the left hemicolectomy along with oncological considerations, to provide both a safe oncological resection along with good postoperative bowel function.The online version contains supplementary material available at 10.1007/s00423-021-02240-7. Splenic flexure (SF) carcinoma represents a technical challenge for the colorectal surgeons both for the difficulty of the anatomical location and dissection from the surrounding organs , but also and above all for the reconstruction and the vascular anatomy of the remaining colon if an extended left hemicolectomy is performed . Surgical options for a SF carcinoma can either be an extended right hemi (and reconstruction by ileo-descending anastomosis), a segmental resection of the SF, or an extended left hemicolectomy which requires a reconstruction with a full Deloyers procedure.While the extended right is a preferable choice for cancers located in the distal third of the transverse colon and/or just above the SF, an extended left hemicolectomy with complete mesocolic excision (CME) along the inferior mesenteric vein (IMV) and inferior mesenteric artery (IMA) seems to be a better option for tumors located in the proximal descending colon and/or just below the SF. The option of performing a segmental resection is usually reserved for elderly patients with tumors located across or just below the SF, due to the thought that the resection will be limited and may not be the best oncological technique Table . HoweverVascular supply and the presence of the middle colic vessels, often represent a technical obstacle to a tension-free and well-vascularized colorectal anastomosis. Deloyers procedure has been described to overcome these anatomical issues, including the sacrifice of the middle colic vessels and involved rotation about the ileocolic pedicle (placing the cecum in the right upper quadrant) or creating a defect in the mesentery and delivering the right colon through to meet the rectum. In selected cases with favorable anatomy and with wider mobilization of the transverse mesocolon, fully up the origin of the middle colic vessels from the superior mesenteric axis, below the body of the pancreas, a modification of the original Deloyers procedure maybe effectively performed with preservation of the middle colic vessels, therefore achieving a tension-free and even better vascularized anastomosis between the proximal transverse colon and the upper rectum.This video vignette presents the case of a fit 67-year-old female  presenting with adenocarcinoma of the proximal descending colon. The cancer was impassable by the endoscope and was extending cranially by 7–8 cm on CT scan up to involving the anatomical zone of SF. She had multiple enlarged lymph nodes (LNs) on CT visible along the inferior mesenteric axis and radiological features of visceral serosa involvement cT4aN + M0). Given her relatively young age, fit conditions, and preoperative features of a locally advanced cancer, she was scheduled for a laparoscopic left extended hemicolectomy (LELC). At laparoscopy, multiple enlarged lymph nodes could be visualized along the IMA and IMV axes. CME with CVL  was performed with IMV taken at the level of the inferior border of the pancreas/D-J flexure and IMA taken flush to the aorta. Extended left hemi was completed with full dissection of the transverse mesocolon, identification and dissection of the middle colic vessels, and preservation of the main trunk and of the right branch of the middle colic artery. A full derotation of the right colon was achieved on the embryological planes on a craniocaudal manner from the proximal transverse colon (TC), hepatic flexure, ascending colon, and caecum. Combination of a full release of the fusion fascia of Toldt’s (right retrocolic fascia) and, in the upper medial continuity, release of Fredet’s fascia (fascia preduodenopancreatica) Fig. , and 29caN + M0. The transverse mesocolon is mobile and not rare, especially in thin patients, which can be redundant if appropriately mobilized. Knowledge of the embryology is of paramount importance: the posterior and superior part of the redundant gastrosplenic ligament fuses to the anterior leaf of the dorsal mesentery of the transverse colon to form the transverse mesocolon. The middle colic vessels mark the location of this structure.The mesenteric component of each flexure is best described in terms of radial and longitudinal axes. The radial axis of the hepatic flexure extends radially from the middle colic vascular pedicle to the intestinal margin of the mesentery. As it does so, the mesentery changes from attached  to non-attached and thus mobile. The longitudinal axis extends longitudinally from the right mesocolon to the transverse mesocolon. At the right mesocolic pole of the longitudinal axis, the mesentery is fully attached across its breadth. At the transverse mesocolic pole of the longitudinal axis, the mesentery is attached centrally but mobile at the intestinal margin. Thus, the mesenteric component of the hepatic flexure undergoes considerable conformational changes. The transverse mesocolon elongates dramatically at the intestinal margin. In this region, and due to elongation, it folds back on to itself and adopts a conformation considerably variable .The transverse mesocolon and colon overlie the small intestinal mesentery, and the greater omentum overlies the upper surface of the transverse mesocolon. Extensive adhesions occur between the under surface of the greater omentum and the upper surface of the transverse mesocolon. As a result, the lesser sac is frequently obliterated where the transverse mesocolon and greater omentum are attached. This arrangement has surgical implications and this is the theoretical basis for our modified technique of allowing the TC to reach the pelvis without the need of sacrificing the middle colic vessels.As occurs in the right and left mesocolon, mesenteric fat is increased around the middle colic artery (the middle colic adipo-vascular pedicle). On either side of this pedicle, the mesentery thins to the point of being translucent in individuals whose body mass index is low  .A crucial step of this modified Deloyers, aiming to easily achieve a full mobilization of the entire length of the transverse mesentery without the need of sacrificing the middle colic vessels, is to procced in a stepwise manner with the following manouvres: performing the dissection of the colo-epiploic attachment close to the upper border of the TC; entering the lesser sac and then gaining adequate extra length of the transverse mesentery for obtaining enough reach up to the pelvic brim, by combined mobilization of the right colon along the Fredet’s fascia and mobilization of the TC by dissecting the root of its mesentery; opening the adhesions between the superior surface of the mesocolon and the inferior border of the pancreas, up to the origin of the middle colic vessels from the SM vesselsA crucial step of this modified Deloyers, aiming to easily achieve a full mobilization of the entire length of the transverse mesentery without the need of sacrificing the middle colic vessels, is to procced in a stepwise manner with the following manouvres: performing the dissection of the colo-epiploic attachment close to the upper border of the TC; entering the lesser sac and then gaining adequate extra length of the transverse mesentery for obtaining enough reach up to the pelvic brim, by combined mobilization of the right colon along the Fredet’s fascia and mobilization of the TC by dissecting the root of its mesentery; opening the adhesions between the superior surface of the mesocolon and the inferior border of the pancreas, up to the origin of the middle colic vessels from the SM vessels Fig. .Fig. 6PaBy avoiding the creation of a defect in the SB mesentery and delivering the right colon through to meet the rectum, one may raise the criticism that the colon is simply fully mobilized, but brought down to the rectal stump by pulling it over the small bowel, thus exposing to the risk of creating an internal hernia which may cause future problems. However, with this modified Deloyers which avoids the creation of a retro-ileal window, the proximal colon is going down to the pelvic brim, by staying over the aortic line and lying just on the left of D-J flexure, while keeping the small bowel loops medially on the right and median abdominal quadrants. Early adhesions between the proximal colonic conduit and the retroperitoneal surface will prevent any internal hernia of small bowel.While laparoscopic extended right colectomy is a well-established procedure, LELC is rarely used  , 3. ReceIn conclusion, we present the rare instance when a laparoscopic derotation of the ascending colon is required to provide a tension-free anastomosis. The resection is completed by delivering of the fully derotated ascending colon and hepatic flexure through a suprapubic mini-Pfannenstiel incision. The primary colorectal anastomosis is subsequently fashioned in a tension-free way and provides for a quick postoperative recovery of the patient. This modified and partial Deloyers procedure preserves the middle colic since the fully mobilized mesocolon allows for a tension-free anastomosis while maintaining better blood supply to the mobilized stump Fig. , c. AlsoLaparoscopic derotation of the right colon is a feasible technique in experienced hands to provide primary anastomosis after LELC with improved functional outcome. Nevertheless, it is important to consider anatomical aspects of the left hemicolectomy along with oncological considerations, to provide both a safe oncological resection along with good postoperative bowel function.Supplementary file1 (PPTX 145079 KB)Supplementary file2 (PPTX 145079 KB)Supplementary file3 (DOCX 20 KB)Supplementary file4 (DOCX 50.5 KB)Below is the link to the electronic supplementary material.