Effective Bearing Fault Diagnosis Research Articles

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Abstract As a critical component in mechanical equipment, rolling bearings play a vital role in industrial production. Effective bearing fault diagnosis provides a more reliable guarantee for the safe operation of the industrial output. Traditional data-driven bearing fault diagnosis methods often have problems such as insufficient fault feature extraction and poor model generalization capabilities, resulting in reduced diagnostic accuracy. To solve these problems and significantly improve the diagnosis accuracy, this paper proposes a novel fault diagnosis method based on multi-resolution singular value decomposition (MRSVD), continuous wavelet transform (CWT), improved convolutional neural network (CNN) enhanced by convolutional block attention module (CBAM), and long short-term memory (LSTM). Through MRSVD, the vibration signal is decomposed layer by layer into multiple denoised signals, thus signal noise can be eliminated to the greatest extent to gain the optimal denoised signals; then through CWT, the optimal denoised signals are converted into two-dimensional time-frequency images so that the local and global characteristic information can be fully captured. Finally, through improved CNN-LSTM, feature extraction is greatly enhanced, resulting in high accuracy of fault diagnosis. Lots of experiments are organized to test the performance, and the experimental results show that the proposed method on various datasets has better diagnosis accuracy and generalization ability under different working conditions than other methods.

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

A review of recent advances in fault diagnosis based on deep neural networks

Bearings are essential components in mechanical systems, supporting the rotation of various machine parts in motors, wind turbines, vehicles, and industrial robots, making their health critical for system performance and reliability. Traditional diagnosis methods, such as vibration and acoustic analysis, along with temperature monitoring, often demand expertise and may struggle to detect early faults. However, the introduction of deep learning technology has created new opportunities for more effective bearing fault diagnosis. The application of deep learning-based bearing fault diagnosis in the industrial sector has gained significant attention and multiple types of deep learning networks have already been successfully implemented. This paper aims to provide a clear review of bearing fault diagnosis based on deep learning algorithms. This essay focuses on two of the most popular deep learning networks, Autoencoder and Convolutional Neural Networks. Their mechanism and applications are analyzed based on essays and research paper related to the field of bearing fault diagnosis. Finally, conclusions are presented to summarize the current development and point out faced challenges and future trends of these deep learning networks. It is also expected that this narrative not only serves as a cogent overview of the contemporary fault diagnosis technologies but also provides convenience and inspiration for further study in this field.

A novel two-stream multi-head self-attention convolutional neural network for bearing fault diagnosis

Bearing is one of the most prone to failure components, and the most commonly used supporting parts in rotating machinery. There are important economic and safety significance for studying effective bearing fault diagnosis method. Deep learning has been widely applied to intelligent fault diagnosis of rotating machines. In particular, convolutional neural network (CNN) improves diagnostic accuracy due to automatic feature extraction ability. However, CNN cannot fully use the temporal information of the input signals and extract global features. This article presents a new depth model called two-stream multi-head self-attention convolutional neural network (TSMSCNN) for bearing fault diagnosis to solve the above problems. First, we obtain temporal information through the difference of data points in the input signal. Next, the temporal information and original signal are used as input for two CNN branches. Then, the multi-head self-attention (MS) mechanism adaptively aggregates the features and assigns weights to the features extracted by CNN. Finally, the learned high-level representations are fed into the full connect (FC) layer for fault diagnosis. Experimental results prove that the proposed TSMSCNN has higher accuracy than state-of-the-art methods and has excellent noise immunity and transfer performance.

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data.

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project 'Integrated Intelligent Bearing Systems' collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain-Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data-all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters.

Fault diagnosis of rolling element bearing using continuous wavelet transform and K- nearest neighbour

Rotating machines are a common class for machineries in industries and one of the root cause of failure of these machines are faults in rolling element bearing (REB). Therefore, to prevent machinery performance degradation and malfunctioning, an effective bearing fault diagnosis technique is vital. So, the use of different machine learning techniques proposes a new method of diagnosing REB defects. Machine learning (ML) method namely K-nearest neighbour (KNN) is used to classify different types of REB faults. In this article, vibration signals are acquired from a customized test rig for three conditions of deep groove ball bearings: Normal (N), defect on Inner Race (IR) and defect on Outer Race (OR) at different loads and maximum speed. Continuous Wavelet Transform (CWT) has been applied to the acquired signal and Seventeen Time Frequency Domain (TFD) statistical features were extracted from CWT coefficients. KNN classifier is trained using these TFD. The classification accuracy on test data i.e., 83.3% reveals that KNN can be effectively used to classify different conditions of REB.

A Feature Selection Approach Hybrid Grey Wolf and Heap-Based Optimizer Applied in Bearing Fault Diagnosis

An effective bearing fault diagnosis model based on machine learning is proposed in this study. The model can separate into three stages: feature extraction, feature selection, and classification. In the stage of feature extraction, multiresolution analysis (MRA) and fast Fourier transform (FFT) are applied to extract the features from the raw signal measured from the rotating machine. In the second stage, a powerful feature selection method is proposed and utilized in the stage of feature selection. The new feature selection method is based on grey wolf optimization (GWO) and heap-based optimizer (HBO) with new strategies combined. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) are used as the classifier independently. To verify the capability of the proposed model, four different datasets are applied to test the model in this study, respectively University of California Irvine (UCI) benchmark dataset, bearing dataset, Case Western Reserve University (CWRU) benchmark dataset, and Machinery Failure Prevention Technology (MFPT) benchmark dataset. The proposed method is compared with the existing methods and can certify the robustness with the experiment results.

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN.

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network

Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

This article presents an effective bearing fault diagnosis model based on multiple extraction and selection techniques. In multiple feature extraction, the discrete wavelet transform, envelope analysis, and fast Fourier transform are considered. While the combined binary particle swarm optimization with extended memory is focusing on feature selection. The current signals are analyzed by discrete wavelet transform. From there, the statistical features in the time and frequency domain are extracted by two techniques: envelope analysis, fast Fourier transform. Subsequently, the binary particle swarm optimization is combined with extended memory and two proposed position update mechanisms to eliminate redundant or irrelevant features to achieve the optimal feature subset. Besides, three classifiers including naïve Bayes, decision tree, and linear discriminant analysis are applied and compared to select the best model to detect the bearing fault.

Intelligent Bearing Fault Diagnosis Using Multi-Head Attention-Based CNN

Aiming at automatic feature extraction and fault recognition of rolling bearings, a new data-driven intelligent fault diagnosis approach using multi-head attention and convolutional neural network (CNN) is proposed. Firstly, a simple signal-to-image spatial transform method is utilized to generate 2D gray images within different health states from raw bearing vibration signals, and generated images are divided randomly into training data and testing data. Secondly, we adopt the multi-head attention mechanism to optimize the CNN structure and develop a new convolutional network model for intelligent bearing fault diagnosis. Next, the training data is used to train network parameters of the designed CNN model to accurately realize bearing fault recognition. Finally, the diagnostic performance of the proposed bearing diagnosis framework is verified on different sub-datasets under working loads of 0~3 hp, and comparison experiments are also conducted with other CNN models and bearing fault diagnosis methods. Experiment results show that the proposed diagnostic method can achieve effective bearing fault diagnosis with relatively fewer CNN model parameters, and has some generalization ability. The recognition rate of bearing states under working loads of 0~3 hp all reach over 99%.

An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.

Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network

Bearings are one of the most crucial components in many industrial machines. Effective bearing fault diagnosis is essential for normal and safe machine operation. Existing fault diagnosis methods are mostly limited to manual feature characterization and traditional machine learning schemes such as support vector machines (SVM) and k-nearest neighbors (k-NN) algorithms. Unfortunately, the interpretation and engineering of such features require substantial human expertise. This paper proposes an adaptive deep convolutional neural network (ADCNN) that utilizes a two-dimensional visualization of the raw acoustic emission (AE) signal to provide bearing health state information, which serves to automate and better generalize the feature extraction and classification process. This 2D visualization tool applies a discrete wavelet packet transform (WPT) and quantifies each sub-band of the signal by defining a new evaluation metric—the degree of defectiveness ratio (DDR)— to precisely represent each fault condition, henceforth called a DDRgram. The motivation for using this DDRgram-based preprocessing scheme is that valuable information regarding rotating components is distributed across discrete frequencies, and thus bearing health conditions can be revealed by those frequency spectra. Furthermore, the proposed ADCNN is trained using an adaptive learning method to achieve improved diagnostic performance. The efficiency of the proposed fault diagnosis methodology (DDRgram + ADCNN) is verified using AE data, collected from a benchmark bearings testbed. The experimental studies demonstrate that the proposed approach outperforms existing state-of-the-art algorithms for the multi-fault classification of bearings with good accuracy.

Simultaneous bearing fault diagnosis and severity detection using a LAMSTAR network‐based approach

Bearings are one of the most important components in many industrial machines. Effective bearing fault diagnosis and severity detection are critical for keeping the machines operate normally and safe. In this study, the problem of simultaneous bearing fault diagnosis and severity detection with deep learning is addressed. Existing solutions developed using deep learning rely on fault feature extraction using complicated signal processing techniques. They perform bearing fault diagnosis and severity detection separately and normally require extensive supervised fine tuning. This study presents an effective deep learning-based solution using a large memory storage and retrieval (LAMSTAR) neural network. The developed approach can automatically extract self-learned fault features and perform bearing fault diagnosis and severity detection simultaneously. The structure of the LAMSTAR network is determined by optimally selecting the sliding box size of the input time–frequency matrix. The effectiveness of the proposed approach is validated using data collected from rolling element bearing tests.

Deep Learning Based Approach for Bearing Fault Diagnosis

Bearing is one of the most critical components in most electrical and power drives. Effective bearing fault diagnosis is important for keeping the electrical and power drives safe and operating normally. In the age of Internet of Things and Industrial 4.0, massive real-time data are collected from bearing health monitoring systems. Mechanical big data have the characteristics of large volume, diversity, and high velocity. There are two major problems in using the existing methods for bearing fault diagnosis with big data. The features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, and the used models have shallow architectures, limiting their capability in fault diagnosis. Effectively mining features from big data and accurately identifying the bearing health conditions with new advanced methods have become new issues. This paper presents a deep learning-based approach for bearing fault diagnosis. The presented approach preprocesses sensor signals using short-time Fourier transform (STFT). Based on a simple spectrum matrix obtained by STFT, an optimized deep learning structure, large memory storage retrieval (LAMSTAR) neural network, is built to diagnose the bearing faults. Acoustic emission signals acquired from a bearing test rig are used to validate the presented method. The validation results show the accurate classification performance on various bearing faults under different working conditions. The performance of the presented method is also compared with other effective bearing fault diagnosis methods reported in the literature. The comparison results have shown that the presented method gives much better diagnostic performance, even at relatively low rotating speeds.

The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum

In vibration analysis, weak fault detect and diagnosis is of great importance. A method based on minimum entropy deconvolution and envelop spectrum analysis is proposed in this article. Minimum entropy deconvolution technique searches for an optimal set of filter coefficients to enhance the impulse making the filtered signal to contain clearer fault information. When there is a fault in bearing, it can be obviously reflected by the power spectral density estimation of the filtered signal even if the power spectral density estimation of original signal denies the information. The filtered signals are then analyzed by performing envelop spectrum analysis, where the bearing characteristic frequencies are quite clear for further diagnosis. The feasibility and validity of utilizing the minimum entropy deconvolution in weak fault diagnosis and condition monitoring is demonstrated by both simulation and experiments. The analysis of actual data from accelerated life test of rolling bearing shows that it can detect and diagnose fault 7 min earlier than RMS can reflect while the entire life takes 1962 min. This is considered to be significant in condition monitoring and early incipient fault diagnosis. The results of gearbox test also demonstrates the effective bearing fault diagnosis under strong noise and gear vibration interactions.