Bearing Fault Classification Research Articles

Partial Transfer Learning of Multidiscriminator Deep Weighted Adversarial Network in Cross-Machine Fault Diagnosis

Deep transfer learning provides a feasible fault diagnosis method for intelligent mechanical systems. However, this method usually assumes that the source domain and the target domain have the same label space, which greatly limits its application in the actual industry. Therefore, this article proposes a multidiscriminator deep weighted adversarial network (MDWAN) method, which is especially suitable for partial transfer learning where the number of target domain categories is less than the source domain. The proposed method is mainly composed of three parts: feature extractor, multidiscriminator, and classifier. In the feature extraction section, a deep separable convolutional neural network model (DSC) is proposed, which can greatly reduce parameter calculation amount under the premise of ensuring the extraction accuracy. In the multidiscriminator section, a multidiscriminator weighted learning strategy is proposed. This strategy comprehensively considers the domain information and source domain label information, and introduces a weight function to quantify the contribution of source domain samples to the domain discriminator and classifier, which can effectively identify and filter outlier source samples to promote the positive transfer of shared samples. In order to verify the effectiveness and feasibility of the proposed method, the method is applied to three types of bearing datasets of different machines. Comparing the classification results of different methods, the conclusion shows that this method is more beneficial for bearing fault classification.

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors.

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.

A novel symplectic relevance matrix machine method for intelligent fault diagnosis of roller bearing

In the fault classification of roller bearing, it is often encountered that the input sample is composed of feature matrix with rich structural information. As a new classifier, support matrix machine (SMM) makes full use of the structural information of input samples to establish prediction model. However, SMM lacks necessary probability information, and its sparsity and robustness are not clear. To resolve the above issues, a new matrix classifier - symplectic relevance matrix machine (SRMM) is proposed based on the probability framework and symplectic geometry theory. In SRMM, the model takes the original signal matrix as the input, and constructs the model elements with rich structural information. Then, symplectic geometry matrix is obtained by symplectic geometry similarity transformation, which makes SRMM robust. Meanwhile, the model elements are constructed by symplectic geometry, which can solve the difficulty of constructing recursive kernel function, reduce the complexity of the model, avoid the time consumption in the process of model parameter optimization. The experimental results of three roller bearing datasets show that SRMM has good classification performance in roller bearing fault diagnosis by comparing recognition rate, time, kappa, accuracy, recall rate and F1 and statistical test.

Design of classification model on high-dimensional imbalance data of motor bearing fault

According to the characteristics of high-dimensional imbalance distribution of motor bearing fault data, a design scheme of classification model is proposed for the high-dimensional data reduction problem in the classification algorithm. For details: Combining standard particle swarm optimization algorithm and random forest algorithm, a new high-dimensional data reduction algorithm is proposed. Aiming at the imbalance problem of data categories in the classification algorithm, we proposes to use machine learning under the sum of squares of dynamic deviations criterion to divide the minority sample data set into mixed regions, high-purity minority sample regions and outlier regions, and then use smote algorithm to complete the data equalization processing, so as to make the sample data equalization processing more reasonable, Focusing on the task of motor bearing fault classification, a design scheme of using standard particle swarm optimization algorithm to improve the least squares support vector machine model is proposed.

Fault diagnosis of wind turbine bearing based on CNN-XGBoost

When a wind turbine bearing fails, because the fault signal shows the characteristics of instability and non-linearity,it is difficult to extract the fault features artificially, resulting in a lower accuracy of the wind turbine bearing fault classification. In order to realize the automatic extraction of wind turbine bearing faults and improve the accuracy of fault classification, this paper proposes a wind turbine bearing fault identification method based on the combination of convolutional neural network and limit gradient lifting algorithm. First, convert the original bearing fault signal into a grayscale image. Then, the gray image is input to the convolutional neural network to automatically extract the feature of the fault. Finally, the limit gradient boosting algorithm optimized by the grid search algorithm is used as a classifier to classify the fault. The simulation results show that the method used in this paper can achieve a high rate of correctness of wind turbine bearing fault identification, indicating that this method is effective for wind turbine bearing fault diagnosis.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

Sparse representation is one of the effective approaches for bearing fault diagnosis. Conventional sparse representation only focuses on fault feature extraction from bearing vibration signals while requiring experts to empirically make a diagnosis. This paper investigates bearing intelligent fault classification based on sparse representation based classification (SRC) method. A novel SRC algorithm, named joint discriminative and shared dictionary learning (JDSDL), is proposed for simultaneously characterize the class-specific and common features in vibration signals of bearings with different faults. The JDSDL seamlessly integrates discriminative dictionary learning, shared dictionary learning, and classifier training into a joint model to automatically recognize bearing faults. Moreover, a dictionary extension strategy is embedded within the proposed JDSDL model to make learned discriminative dictionary shift-invariant to improve its classification performance. The main advantages of the proposed method are that the JDSDL not only works well even if the training set is small, but also is robust to corruption of environment noises. Experimental results on three bearing datasets and comparison with competing methods are presented to demonstrate the effectiveness and superiority of the proposed JDSDL in bearing intelligent fault classification.

Bearing Fault Diagnosis Using a Grad-CAM-Based Convolutional Neuro-Fuzzy Network

When a machine tool is used for a long time, its bearing experiences wear and failure due to heat and vibration, resulting in damage to the machine tool. In order to make the machine tool stable for processing, this paper proposes a smart bearing diagnosis system (SBDS), which uses a gradient-weighted class activation mapping (Grad-CAM)-based convolutional neuro-fuzzy network (GC-CNFN) to detect the bearing status of the machine tool. The developed GC-CNFN is composed of a convolutional layer and neuro-fuzzy network. The convolutional layer can automatically extract vibration signal features, which are then classified using the neuro-fuzzy network. Moreover, Grad-CAM is used to analyze the attention of the diagnosis model. To verify the performance of bearing fault classification, the 1D CNN (ODCNN) and improved 1D LeNet-5 (I1DLeNet) were adopted to compare with the proposed GC-CNFN. Experimental results showed that the proposed GC-CNFN required fewer parameters (20K), had a shorter average calculation time (117.7 s), and had a higher prediction accuracy (99.88%) in bearing fault classification. The proposed SBDS can not only accurately classify bearing faults, but also help users understand the current status of the machine tool.

Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Rolling Bearing Fault Classification Based on Stacked Denoising Auto Encoders

Aiming at the characteristics of large capacity and diversity of rolling bearing fault data, an intelligent rolling bearing fault diagnosis method based on stacked denoising auto encoders was proposed. Firstly, Principal Component Analysis was used to reduce the dimension of the original data, and the redundant information is deleted. Then, three de-noising auto-encoders are created to train the bearing data. Then, a stack de-noising auto-encoder with three hidden layers is stacked with the trained DAE for reverse optimization. Finally, the features are input into soft-max classifier to realize rolling bearing fault diagnosis. The experimental results show that SDAE network can extract fault features effectively, and it is better than back propagation neural network in generalization and fault classification accuracy.

Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features

Electric motors perform the crucial task of converting electrical energy into essential mechanical energy on demand. Motors are plentifully used in the industrial sector all over the world to drive mechanical appliances. Despite being robust and sturdy, motors are not entirely fault-proof, and faults that are caused by the bearings trouble them the most. Early detection of these faults allows engineers to take preventive measures and avert hard breakdowns. Numerous studies have been conducted in this area of research. Many methods have been proposed and implemented to detect the existence and determine the type of fault present in an induction motor. However, this field of research is still open since there is room for improvements in the claimed results. In this paper, a novel fault diagnosis method has been proposed involving an emerging machine learning technique named extreme learning machine to identify the existence of flaws in motor bearings and specify their origins. The described method is tested on a benchmark bearing fault dataset provided by Case Western Reserve University Bearing Data Center. The acquired result yields a maximum classification accuracy of 99.86% and an average classification accuracy of 98.67% after being tested on multiple fault datasets.

Non‐invasive sound‐based classifier of bearing faults in electric induction motors

Induction motors play a major role in the industry nowadays due to their simple construction, uncomplicated maintenance, and cost efficiency. As the motor operates for repeated hours, some faults may occur and, depending on the process sensitivity, can cause significant losses to the industrial production. In this context, an alternative method is proposed to detect and classify bearing faults using acoustic emission signals generated by the machines and simple features obtained from them. A pair of condenser microphones was used to acquire these signals and audio feature extraction is performed to obtain time and frequency patterns to characterize healthy and faulty machines. The major differences of the acoustic signals regarding the fault frequency signatures are discussed by analyzing specific peaks observed in their spectra. Several coupled load values and levels of power supply voltage unbalance were considered in the experimental tests, which emulate common situations encountered in industrial environments, obtaining accuracy rates over 97% of success. Finally, a comparison is presented of machine learning techniques for bearing faults classification under different load values and voltage unbalance levels.

Intelligent Siagnosis Algorithm for Bearing Faults Based on Vibration Potential Features and Affinity Propagation Clustering

In order to achieve intelligent classification of bearing faults, after comparing a large number of mechanical fault signal features, this paper proposes a bearing intelligent diagnosis algorithm based on vibration potential energy feature extraction and AP clustering. The potential energy features are extracted from the multidimensional eigenmode function (IMF) of the vibration signal after the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and after self-weighted feature selection, the Affinity Propagation Clustering (AP) algorithm is used to achieve accurate classification of unlabeled faulty bearings. After data validation, the method can be better applied to the fault classification of rolling bearings, and the performance is better than the traditional classification and diagnosis algorithm. The algorithm is used to guide intelligent fault diagnosis of unlabeled data and to improve the applicability of AP clustering algorithm in rotating machinery fault diagnosis.

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

Bearing Fault Diagnosis Based on Adaptive Convolutional Neural Network With Nesterov Momentum

It is difficult to achieve satisfactory classification results for bearing fault diagnosis methods based on prior knowledge. This paper presents an adaptive convolution neural network based on Nesterov momentum for rolling bearing fault diagnosis. Firstly, the traditional momentum method in the network is replaced by Nesterov momentum. Nesterov momentum can predict the falling position of parameters and adjust the parameters in advance, to avoid the problem that the traditional momentum method is likely to miss the optimal solution. Secondly, in order to improve the generalization ability of the network, an adaptive learning rate rule which dynamically adjusts the learning rate according to the rate of error change is proposed. Finally, the original vibration signals are directly inputted into the proposed network to train the fault diagnosis model, and the test data are used to evaluate the model. The experimental results show that compared with the traditional convolutional neural network, the proposed method improves the convergence of the neural network and effectively improves the accuracy of bearing fault classification.

Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis

The high applicability of the electrical motor has led to gain attention in condition monitoring to diagnosis the most common type of fault in this machine, bearing element. The emergence of deep neural networks (DNN) provides the opportunity to design a network for early bearing fault diagnosis with high speed and without any additional feature extraction technique. However, robustness against the noise and some deficiencies in fully capturing features are still challenging issues. To resolve this problem, this paper proposes a one module Gabor filter based convolutional neural networks (CNN), namely Gabor convolutional neural network (GCNN), for bearing fault detection and classification. GCNN is a modulated Gabor filter to enhance the ability in capturing temporal features as well as enhance understanding spatial features with fewer parameters and higher robustness against noises and can be considered as a computational efficient deep structure. The simulation results of the bearing fault detection/classification are studied on two different experimental prototypes, including case Western Reserve University (CWRU) and Paderborn University (Paderborn) datasets. The superiority of this method is shown by comparison with accelerated CNN (ACNN), adaptive CNN, standard CNN, support vector machine (SVM), learning vector quantisation (LVQ), and feedforward neural network (FFNN).

Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification

Rolling element bearings are crucial, of high failure rate and easy damage parts of rotating machinery, and significantly affect safe and reliable production processes. Much more attention has been focused on fault diagnosis of rolling element bearings in recent years. This article presents a novel feature extraction scheme for the classification of multiple bearing faults. Multi-Scale Sample Entropy (M-SSampEn) is combined with Energy Moment (EM) to construct a time-domain Multi-Scale Sample Entropy-based Energy Moment (M-SSampEn-EM) feature extractor. The classifier model for the proposed fault classification system has been built using the Least Square Support Vector Machine (LS-SVM). The M-SSampEn-EM feature extractor is used to capture two-dimensional representative eigenvectors from multiple fault classes' bearing vibration data. Its separability performance is ensured by optimizing the feature-extraction parameters, including the Embedded Dimension and the Tolerance, etc. The LS-SVM classifier is also compared with two Neural Network (NN)-based classifiers, i.e., Radial Basis Function NN (RBFNN) and Probabilistic NN (PNN), to show it better generalization performance on bearing fault classification. The experimental study verifies the excellent capacity of the proposed approach in bearing fault classification.

A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features

The key to intelligent fault diagnosis is to find relevant characteristics with the capability of representing different types of faults. However, the engineering problem is that a few simple empirical features (EFs) cannot obtain high classification accuracy, and complex feature engineering requires strong professional knowledge, which leads to limited applications on a general scale. In addition, intelligent feature extraction and classification methods without prior knowledge cannot guarantee that the model learned the general features used for classification, and its robustness and generalization are not strong when the objects with low-quality training data. Therefore, a fusion method of combining EFs and adaptive features extracted by a deep neural network is proposed. In this method, simple EFs that only need a few professional knowledge are adopted to realize general feature extraction and, hence, maintain the robustness of the model. A modified neural network structure (LiftingNet) is proposed to achieve the adaptive extraction of hidden features for specific objects, for realizing the high precision of bearing fault classification. In order to realize the fusing of EFs and adaptive features, XGBoost is utilized as the final classifier instead of common softmax. The feasibility and validity of the proposed method are verified by two data sets collected from motor bearings at stable work conditions. The experimental results show that the classification accuracy generated by the proposed method is improved. It also can maintain the robust performance on data sets even with various noises.

Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

Feature extraction from a time sequence signal without manual information is an important part for bearing intelligent diagnosis. With the merits of signal information and feature structure information excavation, Deep ConvNet is widely used in bearing fault diagnosis and analysis under complex working conditions. However, due to the complexity of the bearing operating environment in the actual operation process, the sensitive features show different scale distribution characteristics. Meanwhile, it is known that the convolution kernel of ConvNet is usually small, which mainly focuses on the small-scale details of state distribution characteristics while ignores the identification of the overall trend of characteristic distribution. Considering that the size of convolution kernel can sense information hidden in different scales, this paper designed a one-dimensional vision ConvNet (VCN), where the architecture is composed of multilayer small kernel network and single-layer large kernel network side by side. The multi-kernel structure improves the ability of network to detect fault characteristic frequency band. By analyzing the artificially generated data and experimental data, the setting method of large convolution kernel and stride is discussed. Compared with the traditional CNN, wide first-layer kernels (WDCNN) and multiscale kernel-based ResCNN (MK-ResCNN), this network improves the recognition accuracy with a better stable training process for rolling bearing fault classification.