Fault Classification Performance Research Articles

Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy

Most existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes a multistage fault detection scheme for rolling bearings. In the first stage, the sensitivity of the range entropy to bearing failure is used to define a threshold, based on which the health status of the bearing is judged. If the unknown bearing is judged to be faulty, the next stage is implemented. In the second stage, a fault feature extraction method based on dual-tree complex wavelet packet transform (DTCWPT), time-shifted multiscale range entropy (TSMRE), and t-distributed stochastic neighbor embedding (t-SNE) is proposed, and a random forest (RF) discriminator is used for fault classification. To achieve the desired performance of fault classification, a new coarsening approach for complexity measurement called TSMRE is developed on the basis of the range entropy (RE). First, the RE value of each time-shifted coarse-grained time series is calculated, and the TSMRE is obtained by averaging the entropy values. The TSMRE improves the coarse-graining processing of the MRE and enhances the stability and reliability of the algorithm. In addition, it can obtain more information from short time series using the time-shifted coarse-grained technology. Therefore, it is less dependent on the length of the original time series. Two sets of rolling bearing data are used for this experiment. The fault recognition rate of each category of samples is 100%. Therefore, the proposed multistage fault diagnosis method can pre-screen healthy bearings and accurately identify the failure types of faulty bearings.

Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet

Traditional fault diagnosis approaches of rolling bearing often need abundant labeled data in advance while some certain fault data are difficult to be acquired in engineering scenarios. This imbalanced fault data problem limits the diagnostic performance. To solve it, an imbalanced fault diagnosis approach based on improved multi-scale residual generative adversarial network (GAN) and feature enhancement-driven capsule network is proposed in this paper. Firstly, frequency slicing wavelet transform is utilized to extract two-dimensional time–frequency features from original vibration signals. By designing multi-scale residual network structure and hybrid loss function, original GAN model is improved, generating high-quality fake time–frequency features to balance fault data distribution. To increase the attention of the diagnostic model to fault-sensitive features and suppress irrelevant features, a feature enhancement network is designed to dynamically weight the fault features by modeling the feature importance. On this basis, enhanced performance of imbalanced fault classification is achieved. Verification experiments demonstrate that it performs well in processing the imbalanced fault data, and has better stability and diagnostic accuracy than state-of-the-art methods.

Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method.

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time.

A Transfer Learning Based Unmanned Aerial Vehicle MEMS Inertial Sensors Fault Diagnosis Method

In this paper, we propose a novel transfer learning based micro-electromechanical system (MEMS) inertial sensors fault diagnosis method. First, the MEMS inertial sensors fault diagnosis method is formulated to a deep transfer learning problem in which the offline samples are deemed as source domain and the online samples are set to target domain features. Second, the bidirectional long short-term memory and Hilbert-Huang transformation-based feature transfer model is designed to decrease the discrepancy between SD and TD, that performs the transfer operation using intrinsic mode function features. Then we propose a convolutional neuro network-based transfer learning algorithm to further decrease deep features discrepancy and perform the fault classification tasks on TD. According to the experiments, the proposed FD method has achieved excellent fault classification performance and significantly improvement comparing with the state-of-the art methods.

Composite fault diagnosis methodology for urban vehicular ad hoc network

Vehicular Ad-hoc NETwork (VANET) is a meteoric growing research area due to the technological advancement in sensing, computation, communication, and radio wireless technology. The demand for VANET is increasing day by day due to the numerous diverse applications. Applications of VANET include safety applications like reduction in the road accident by broadcasting messages to the driver, convenience applications like automatic parking service, commercial applications like selling and buying of products through a network of wheels, productive applications like automatic environmental parameters monitoring, vehicular cloud service like secure toll transaction, etc. The aforementioned VANET applications will become non-operational if the communication unit of the vehicle becomes inoperative or malfunctioning. This paper proposes a composite fault diagnosis methodology by detecting and classifying the fault of the On Board Unit (OBU) of the vehicular ad hoc network. The fault detection includes the detection of two categories of the fault i.e. hard fault in which the communication unit is in the inactive state and not able to communicate with other communication units and soft fault in which the communication unit is in the active state but communicates incorrect data with other communication devices. The hard fault is detected by the repeated time out mechanism and by a statistical method called Kolmogorov–Smirnov test (K-S test). The detection of soft fault (permanent, intermittent, and transient) is performed by a statistical method called the Chi-square test. Apart from the detection of the soft fault, the classification of soft fault is carried out by ECOC-SVM. Finally, the result of the fault is transmitted to the faulty vehicle. The parameters such as Fault Detection Accuracy (FDA) and False Alarm Rate (FAR) are used to validate the proposed fault detection protocol. The parameter False classification rate (FCR) is used to measure the performance of the soft fault classification. Finally, the performance of the transmission of the result to the faulty vehicle is evaluated through the parameters end-to-end (E2E) delay, number of hops, and network gaps by comparing the proposed work with existing routing protocols of VANET like GSR, A-STAR, Bhoi et al.

Vibration based Data Analysis of Single Acting Compressor through Condition Monitoring and Multilayer Perceptron – A Machine Learning Classifier

The air compressor is one of the desired mechanical equipment used for producing compressed air, which is utilized for performing various industrial and domestic functions. Its operation involves several rotating and fluctuating members which fail due to several miscellaneous reasons as the members prone to dynamic working environment quite frequently. The deficiencies create huge impact over the overall performance and thus leads to economic losses associated with system seizure. It is now essential to predict the occurrence of faults at earlier stages in order to avoid major shutdowns. Hence, in this article, a data modelling study using a machine learning algorithm is proposed. Initially, the vibration signals are measured as physical parameters from the compressor test rig as it contains critical information regarding the system working conditions instantly. The statistical features were extracted from the acquired signals and by using the J48 algorithm the most prominent features were selected. These selected features were classified using Multilayer Perceptron and its performance in fault classification was presented

Convolutional Neural Network With Automatic Learning Rate Scheduler for Fault Classification

Fault classification is vital in smart manufacturing, and convolutional neural network (CNN) has been widely applied in fault classification. But the performance of CNN heavily depends on its learning rate. As the default setting on learning rate cannot guarantee its performance, the learning rate tuning process becomes essential. However, the traditional learning rate tuning methods either cost much time consumption or rely on the experts’ experiences, so it is a considerable barrier for the users. To overcome this drawback, this article proposes a CNN with automatic learning rate scheduler (AutoLR-CNN) for fault classification. First, the long short-term memory (LSTM) is used to extract the features of the past loss of CNN. Then, an agent based on deep deterministic policy gradient (DDPG) is trained to automatically control the learning rate for CNN online. Third, the double CNN structure is developed to enhance the stability of the proposed method. The proposed AutoLR-CNN is tested on two famous bearing data sets and a practical bearing data set on wind turbine. The results of AutoLR-CNN are superior to six commonly used baseline learning rate schedulers in Tensorflow. AutoLR-CNN is also compared with other reported machine learning and deep learning methods. The results show that AutoLR-CNN has achieved the state-of-the-art performance in fault classification.

Adaptive Densely Connected Convolutional Auto-Encoder-Based Feature Learning of Gearbox Vibration Signals

Vibration signals are widely utilized for machinery fault diagnosis. Typical deep neural networks (DNNs), e.g., convolutional neural networks (CNNs), perform well in feature learning from vibration signals and have been applied in gearbox fault diagnosis. However, the supervised-learning-based DNNs depend on a large amount of labeled data, and the extracted features consist of much noise and redundant information. In this article, a new DNN, adaptive densely connected convolutional auto-encoder (ADCAE), is proposed for feature extraction from 1-D vibration signals directly in an unsupervised-learning way. First, adaptive attention mechanism (AAM) is proposed for feature filtering. Second, a multiscale convolution based on AAM is proposed for fusion of multiscale information. Moreover, a new unsupervised-learning network, densely connected convolutional auto-encoder (DCAE), is further developed to improve the information flow between encoder and decoder, which significantly improves the performance of feature learning and fault classification. The results on two cases show that ADCAE has good feature extraction performance on gearbox vibration signals. It performs quite better on gearbox fault diagnosis than typical DNNs, e.g., residual CNN (ResNet), densely connected CNN (DenseNet).

Scalable fault models for diagnosis in a synchronous generator using feature mapping and transformation techniques

Condition based maintenance (CBM) needs data acquired during healthy and faulty conditions to develop intelligent system for fault diagnosis. However, fault injection is not allowed/possible in a highly expensive components of complex/critical systems to collect fault condition data. Therefore, proto-type/small working models are used to conduct experiments for abnormal/fault conditions, to obtain and scale the intelligence of the system for effective health monitoring of complex system. This methodology is referred as scalable fault models. For proof of concept, in this work, we considered two different capacity synchronous generators with rating of 3 kVA and 5 kVA to emulate the behavior of prototype/small working model and complex system respectively, for scalable fault models. We explored feature mapping and transformation techniques to achieve effective scalability.From the preliminary experiments, it is observed that the baseline system performance deteriorated due to the changes in the system (capacity) and its characteristics with load changes.We therefore, expressed the input features in terms of load and system independent manner, to make the features less dependent on load and system variations. We explored localityconstrained linear coding (LLC) to express the features load/system independently. It is observed that experimenting LLC with the backend support vector machine (SVM) classifier gave the best fault classification performance for linear kernel, suggesting that the faults are linearly separable in the new feature space.Since the LLC mapped feature space is linearly separable, we then explored linear feature transformation technique, nuisance attribute projection (NAP) on the LLC mapped feature space to further minimize the load/system specific variations. We observed that LLC-NAP improved the overall accuracy and sensitivity of the classifier significantly. We also noted that the performance of NAP was limited in the original feature space since the feature space (NAP without LLC) is nonlinear with load/system variations.

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

AbstractIn this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

Data-driven fault diagnosis of bogie suspension components with on- board acoustic sensors

This paper proposes a data-driven approach for fault-detection and isolation of bogie suspension components with on-board acoustic sensors. The fault detection technique is based on the acoustic emissions variation due to structural modal coupling changes in the presence of faulty components. A suspensions component failure introduces an imbalance into the system, resulting in dynamics interferences between the motions. These interferences modify the energy introduced into the system as well as its acoustic emissions. The unknown arbitrary track irregularities generate together with a variable train speed a random nonstationary vehicle excitation. Speech recognition techniques were used to generate features that consider this phenomenon. Frequency spectrums were analysed in different operating conditions to design efficient features. The robustness of the methodology was verified with data from two different test measurement campaigns on a test ring, where the influence of the sensor locations for the fault classification process was studied. The proposed methodology achieved good fault classification performance on the investigated use cases, removed dampers and 50% damper degradation on primary and secondary vertical suspension.

Data Augmentation Classifier for Imbalanced Fault Classification

The problem of fault classification in industry has been studied extensively. Most classification algorithms are modeled on the premise of data balance. However, the difficulty of collecting industrial data in different modes is quite different. This inevitably leads to data imbalance, which will adversely affect the fault classification performance. This article proposes a novel data augmentation classifier (DAC) for imbalanced fault classification. Data augmentation based on generative adversarial networks (GANs) is an effective way to solve the problem of unbalanced classification. However, the randomness of the GAN generation process restricts the effect of data enhancement. DAC proposes a data selection strategy based on data filtering and data purification in model training to solve this problem. In addition, DAC combines supervised learning and data generation processes to obtain an end-to-end model. Meanwhile, multigenerator structure of DAC (MDAC) is proposed to solve the problem of incomplete learning of a single generator when data imbalances get complicated. The proposed DAC and MDAC are applied in two fault classification cases of the Tennessee Eastman (TE) benchmark process, results of which show superiority of DAC and MDAC compared to existing methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Data imbalances are common in fault classification and affect the effectiveness of modeling in industry. As a generative model, generative adversarial networks (GANs) provide new ideas for small-class data augmentation. However, the instability of its training process and the randomness of data generation affect the results of data augmentation. In this article, the GAN generation process is analyzed in detail. The results of the visualization indicate that no data generation was perfect at any one time. Based on the rules of GAN data generation, we propose a data selection strategy during training. High-quality data are selected for data augmentation through data filtering and data purification. Apart from this, we combine the training process of GAN and classification model for imbalanced data to reduce modeling time. Through industrial examples, we have evaluated the effectiveness of this method.

LDA-based deep transfer learning for fault diagnosis in industrial chemical processes

Deep transfer network (DTN) has been widely used for classification tasks, which introduces maximum mean discrepancy (MMD) based loss function to extract similar latent features and reduce the discrepancy of distributions across the source and target data. However, it is a little challenging to apply deep transfer learning for fault classification tasks in industrial chemical processes, since process variables have physicochemical properties and occupy different impacts in reflecting the process status. Hence, features extracted from these process variables also have different contributions for domain adaptation in DTN. In this paper, a linear discriminant analysis (LDA)–based DTN is proposed for fault classification ofchemical processes, in which a weighted MMD is designed for domain adaptation. First, the LDA algorithm is introduced to determine how much influence each variable can distinguish samples from source and target domains. Then, a corresponding weight is assigned to each feature variable to design the weighted MMD for network transferring. A Tennessee Eastman (TE) process and a real hydrocracking process are used to validate the fault classification performance of the LDA-based DTN. The results indicate LDA-based DTN has better generalization performance and classification accuracy than traditional DTN.

Signal Enhancement Method for Mechanical Fault Diagnosis in Flexible Drive-Train

Nowadays, mechanical fault detection techniques based on signals from motor drive are becoming popular. However, in electromechanical system with flexible drive-train, mechanical fault signature reflected in motor drive is suppressed by the transmission path. This signal attenuation reduces the effectiveness of fault detection. Therefore, research on the law of fault signature transmission from load to motor is necessary. In this article, a two-mass electromechanical system with a long shaft is concerned. First, the transmission path from load torque to electromagnetic torque is analyzed, and the concept of torque transmission efficiency is proposed. Then, the frequency characteristic method and transfer function method are presented to establish the signal transmission model. Finally, the model-based signal enhancement method is proposed to compensate the attenuation. Simulation and experimental results show that the proposed method can enhance the quality of fault signature transmission and improve the performance of fault classification.

Performance Improvement of Feature-Based Fault Classification for Rotor System

For the management of rotating machines, machine learning (ML) has been researched with the use of feature parameters that have physical and statistical meanings of vibration signals. Genetic algorithm (GA) and principal component analysis (PCA) are the algorithms used for the selection or extraction process of the features; equipment condition. This study proposes a new method to maximize the advantages of the extraction and selection algorithms, thereby improving the fault classification performance. The proposed method is estimated in a variety of equipment conditions by selecting and extracting the effective features for status classification. To evaluate the performance of the fault classification through feature selection and extraction of the ML, a comparative analysis with the proposed method and the original method is also performed. With Lab-scale gearbox, several types of fault tests are conducted, and seven different fault types of equipment conditions, including the normal status, are simulated. The results of the experiments show that, the performance of classification of GA for feature selection is 85%, while PCA for feature extraction is 53%. The performance result of the proposed method for fault classification is 95%, meaning that the performance of fault diagnosis is more efficient in terms of discriminative learning than the original method. Therefore, the proposed method with feature extraction and selection algorithm can improve the fault classification performance by 10% and more for fault diagnosis through ML.

Sparse representation-based classification for the planetary gearbox with improved KPCA and dictionary learning

ABSTRACT A fault diagnosis method for the planetary gearbox according to sparse representation-based classification (SRC) has been presented in this paper. Considering the real-time performance and accuracy rate of the fault diagnosis, the proposed method has introduced the improved kernel principal component analysis (KPCA) and dictionary learning. First, some time domain and frequency domain features are combined into a feature vector to represent a sample, which can reduce the computational burden and enhance the real-time performance of fault classification. Second, the feature sets are transformed into a new feature space through the improved KPCA, which can improve the precision of fault classification. Then, the training samples are used to implement dictionary learning, and the testing samples are taken as the input of the SRC for classifying. Finally, a planetary gearbox fault diagnosis experiment is designed to verify the effectiveness of the proposed method.

Modified Label Propagation on Manifold With Applications to Fault Classification

In process monitoring, fault classification performance heavily relies on the labels of training data. However, the labeled data are inadequate and difficult to obtain because they require experienced human annotators. In this paper, a modified label propagation (MLP) method is proposed to propagate labels from labeled data to unlabeled data. The proposed label propagation algorithm has the following advantages: (1) It constructs a global and local consistency framework with the aid of a data graph, manifold learning, and data labels. This framework follows the assumption that data on the manifold will have similar structures, and nearby data will have similar labels. (2) Considering the inner relationship between the unlabeled data and historical data, a new definition for the initial label matrix is offered, which is significant for label propagation. (3) The new method propagates labels in a low-dimensional manifold space, which is different from most existing label propagation methods that propagate them in the original space. The results reveal that under the global and local consistency framework, soft labels of unlabeled data are given more effective predictions. With additional soft labels of unlabeled data, the MLP-based fault classification method is introduced. The simulation results obtained using a toy example demonstrate the label propagation performance of the MLP, and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method.

Deep ensemble forests for industrial fault classification

Taking into account different data characteristics and assumptions, it is hard to define a single classifier that can achieve desired fault classification performance under different circumstances. To tackle this problem, a feature extraction and feature selection based deep ensemble forests model is proposed in this paper, which uses XGBoost, random forests and extremely randomized trees as basic forests in each layer to improve the diversity and the accuracy. In this model, the input of each layer is the concatenation of class probability vector produced by the previous layer and the selected feature vector. The cascading process will be automatically terminated while the performance of the layer no longer increases. The feature importance obtained at first layer is used for feature selection, which is able to make the model more efficient and avoid overfitting. Furthermore, a weighted probability fusion method is employed at the last layer for the final decision. A case study on Tennessee Eastman (TE) benchmark process is conducted and gives a comparison between proposed method and conventional methods.

Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems

Fault detection and diagnosis (FDD) framework is one of safety aspects that is important to the industrial sector to ensure its high-quality production and processes. However, the development of FDD system in chemical process systems could have difficulties, e.g. highly nonlinear correlation within the variables, highly complex process, and an enormous number of sensors to be monitored. These issues have encouraged the development of various approaches to increase the effectiveness and robustness of the FDD framework, such as the wavelet transform analysis, where it has the advantage in extracting the significant features in both time and frequency domain. It has motivated us to propose an extension work of the multi-scale KFDA method, where we have modified it with the implementation of Parseval’s theorem and the application of ANFIS method to improve the performance of the fault classification. In this work, through the implementation of Parseval’s theorem, the observation of fault features via the energy spectrum and effective reduction in DWT analysis data quantity can be accomplished. The extracted features from the multi-scale KFDA method are used for fault diagnosis and classification, where multiple ANFIS models were developed for each designated fault pattern to increase the classification accuracy and reduce the diagnosis error rate. The fault classification performance of the proposed framework has been evaluated using a benchmarked Tennessee Eastman process. The results indicated that the proposed multi-scale KFDA-ANFIS framework has shown the improvement with an average of 87.02% in classification accuracy over the multi-scale PCA-ANFIS (78.90%) and FDA-ANFIS (70.80%).