Bearing Fault Identification Research Articles

A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Fault identification of rolling bearings plays a crucial role in maintaining the efficient and stable operation of equipment. Although traditional fault identification methods have made certain progress, they still lack in model feature extraction capabilities and generalization ability. In this paper, a frequency channel-attention based vision Transformer method is proposed for rolling bearings intelligent fault identification. Using frequency domain channel-attention mechanism, the proposed method is able to preserve fundamental fault information and integrate the frequency characteristics of the vibration signals. The proposed method also leverages the inherent self-attention mechanism of vision Transformer to recognize long-range dependencies within the signal data. This integration of attention not only enhances the model’s sensitivity to signal frequency characteristics but also enables the visualization of the attention mechanism, thereby increasing the model’s interpretability. Additionally, a shift linear layer is proposed to reduce the model’s computational demands while maintaining its robust feature extraction capabilities. This proposed method directly uses the collected vibration raw signals to achieve precise fault identification of rolling bearings, and experimental validation on two datasets demonstrates the model’s diagnostic accuracy under across working conditions

Thrust ball bearing fault identification from visualized data using optimized deep network

Bearings are the most common components employed in machine parts. The bearings' movement facilitates the smooth motion. They also help with friction reduction. The faults in bearings are often caused by tribological parameters. Various methods have been developed to identify faults in bearings, but they often fail to predict these accurately. This work has concentrated on designing an effective fault-recognizing model. Therefore, a framework, the Zebra-based Radial Basis Prediction Mechanism (ZbRBPM), was proposed in this work. Initially, the bearing datasets are collected, and mineral oil (MO) lubrication is added to minimize wear and friction. The primary goal of the work is to detect and classify the fault in the thrust ball bearing. The bearing vibration data included a normal vibration signal, a ball fault, and inner and outer race faults. Hence, Zebra fitness is enhanced for the optimization of tribological parameters and fault identification. The proposed model is executed in the MATLAB system. Finally, performance criteria like accuracy, precision, recall, F-score, error rate, computation time, speed, specific wear rate, friction torque, and energy consumption are validated. The performance of the proposed ZbRBPM gains better accuracy rate as 99.5 %, 99 % of precision and f-score and 99.4 % of recall. Also it significantly reduced the prediction error rate into 0.5 % with lower computation time and very low wear and friction.

LSTA-Net framework: Pioneering intelligent diagnostics for insulating bearings under real-world complex operational conditions and its interpretability

Deep Learning has been attracting considerable attention as it can autonomously learn important signal features and has shown great potential for fault diagnosis. However, given the variability of high-power variable-frequency industrial systems, especially under unfavorable service conditions such as voltage and load fluctuations, the identification of insulated bearing faults in variable-frequency motors has become a key challenge to be addressed. Moreover, the time-varying nature of insulated bearing data poses common technical difficulties for existing models. To bridge these gaps, this research spearheaded the development of a lightweight spatial–temporal focusing framework equipped with a novel optimizer called LSTA-Net, which was designed to address the challenging problem of insulated bearing fault identification in real-world engineering scenarios. Specifically, this involves an innovative design of the Weight Reducing Recursive Unit (WDRU) strategy and an updated backpropagation formulation, which was skillfully applied to the feature extraction module of the LSTA-Net framework for the first time. Furthermore, a novel optimizer called Stochastic Newton Descent (SND) has been developed and skillfully integrated into the LSTA-Net framework with the newly derived weight update formula, providing unique insights to improve diagnostic performance. Finally, the diagnostic performance of the LSTA-Net framework was evaluated from multiple dimensions based on the same dataset, proving its superiority, generalizability, and robustness. t-SNE technique integration intuitively explains the framework’s fault characterization mining process, improving its credibility, reliability, and accuracy.

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

The Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.

Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network

Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network

A novel subdomain adaptive intelligent fault diagnosis method based on multiscale adaptive residual networks

Traditional fault diagnosis methods may not capture key information during feature extraction due to the large distribution difference under different working conditions, which can result in poor accuracy of the diagnostic model. To address this issue, a subdomain adaptive bearing fault identification method guided by the local maximal mean difference (LMMD) under the multiscale adaptive residual network is proposed in this paper. The bearing vibration signals are preprocessed by wavelet convolution and wide convolution to generate initial features. Then, the multi-scale adaptive residual network is used to adjust the feature weights of different scales and extract richer feature information. To reduce the intra-class distribution difference, the LMMD is employed. Additionally, local interclass maximum mean difference (LIMMD) is used to increase the inter-class difference, preventing misclassification of samples from different classes due to their close proximity and achieving sub-domain distribution alignment. The fault diagnosis performance of the domain distance metric model guided by LMMD and LIMMD under the multiscale adaptive residual network is verified through two different bearing model validation experiments.

CMAFI — Copula-based Multifeature Autocorrelation Fault Identification of rolling bearing

Diagnosing the rolling bearings using vibration signals requires advanced signal analysis methods. The envelope spectrum analysis method is commonly used, but it requires the determination of the frequency band in which the signal modulation occurs. This paper proposes a method that does not have such limitations and it is based on copula technique applied to wheel bearing signals. Our approach allows to efficiently identify faults using low energy components of the signal estimated via the PCA method.While it is standard to analyze time series autocorrelation function, proposed multifeature autocorrelation analysis finds multiple dominating contribution to joint distribution of values shifted by various time lags. This way it finds also more subtle statistical dependencies, here suggesting damage of bearing. Proposed approach was verified in the laboratory conditions for different kinds of defaults in different positions.

Bearing Fault Identification Based on CEEMDAN-SVD and CNN

Bearing Fault Identification Based on CEEMDAN-SVD and CNN

An enhanced spline-based time-varying filtering method and fast MKurtgram for bearing fault identification under random impulsive environment

In certain engineering problems, the presence of random impulsive components can heavily corrupt a signal. Thus, random impulsive components are also known as random impulsive noise or non-Gaussian noise. Commonly used signal processing tools in fault diagnosis may perform poorly under such operational conditions. To this end, an alternative approach integrating time-varying filtering with fast MKurtgram (FMK) is proposed as a solution. The FMK is developed based on the fast kurtogram (FK). The FMK addresses a major drawback of the FK, which is vulnerable to random impulsive noise, thus FMK exhibits good performance in impulsive environments. However, the FMK may produce inaccurate estimates of the bandwidth and center frequency when random impulsive noise is relatively intensive. To alleviate this, a spline-based time-varying filtering (TVF) method is designed in this work. This spline-based TVF method is highly effective in eliminating a large amount of Gaussian noise, as well as attenuating the energy of the impulsive components. After undergoing this pre-treatment, the FMK can estimate both bandwidth and center frequency more precisely. The proposed approach is demonstrated to be effective through the simulated and real bearing fault signals.

An improved parameterless empirical wavelet transform for incipient fault identification of wheelset bearing.

The empirical wavelet transform (EWT), along with its adaptable spectrum segmentation technique, finds extensive application in the incipient detection of rolling bearing faults. However, determining mode boundaries adaptively under strong noise interference remains a substantial challenge. Herein, an improved parameterless EWT based on the order statistics filter (OSF) is proposed to overcome this shortcoming. This approach replaces the Fourier spectrum with its envelope spectrum through OSF, and the local minima of the envelope spectrum are selected as the initial boundary to obtain the initial empirical modes. Furthermore, the adjacent initial empirical modes are combined using Pearson's correlation coefficient, and the final number and boundaries of empirical modes are automatically determined using the mean envelope entropy. The advantages of the proposed method are demonstrated through an accelerated degradation bearing test bench and a wheelset-bearing test bench, as well as by comparing it with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and Autogram.

Transformer-based meta learning method for bearing fault identification under multiple small sample conditions

Most fault identification methods based on deep learning rely on a large amount of data, and their effects are limited in the actual production environment. In the case of multiple classification, limited data and complex working conditions, the bearing fault identification is very difficult. In this paper, a novel method called ensemble transformer meta-learning (ETML) is proposed for bearing fault identification with few samples. Based on the base-learner of vision transformer (ViT) and the meta-learner of model-agnostic meta-learning (MAML), a novel transformer meta-learning model is constructed to enhance the extracting feature ability. Finally, the weighted voting of multiple models determines the final result. By meta-training of limited data, the model can obtain excellent initial parameters and identify bearing faults quickly and accurately with fewer samples under varying working conditions. The effectiveness of ETML is verified by two cases of bearing fault identification with a few labeled fault samples.

A new rolling bearing fault diagnoses method based on period-doubling bifurcation in the Hindmarsh–Rose model

The Hindmarsh–Rose (HR) model is a three-dimensional oscillators susceptible to initial values, making it capable of amplifying even the slightest variations. On this basis, we proposed a rolling bearing fault identification method based on period-doubling bifurcation in the HR model and constructed a bearing fault experimental platform to validate our approach in this paper. Initially, we analyze the HR model’s bifurcation characteristics using the discrete mapping method to identify oscillators suitable for detecting bearing faults. We then select the multiplicative period bifurcation points of the HR model to differentiate between different types of bearing faults. Next, we decompose and reconstruct vibration signals using the Hilbert–Huang transform and calculate the amplitude characteristics of the fault frequency band as the input for the HR detection oscillator. Finally, bearing faults are identified based on the phase trajectory of period-doubling. Furthermore, a comparative analysis is conducted between the proposed methodology and the employment of the empirical wavelet transform. Our approach presents a new perspective for utilizing nonlinear oscillators in bearing fault diagnosis.

Phase space visibility graph

We introduce a topological approach for quantifying the dynamical complexity of time series. A novel complex network of visibility graph family is proposed based on defining visibility algorithm in phase space. The statistical properties of the constructed network show powerful potentiality for distinguishing stochastic and chaotic systems. And for some remarkable chaotic systems, it allows for quantitative correspondence with Lyapunov exponents. The potential practical application of this approach is demonstrated on the multiphase flow system and bearing fault identification. This work paves a natural way constructing complex network in phase space, and provides a complexity quantitative estimate for chaotic time series.

Application of improved bubble entropy and machine learning in the adaptive diagnosis of rotating machinery faults

This paper proposes an improved bubble entropy algorithm called Improved Hierarchical Refined Composite Multiscale Multichannel Bubble Entropy (IHRCMMCBE) to characterize the fault characteristics of rotating machinery. By introducing the refined composite multiscale analysis algorithm, the improved hierarchical decomposition algorithm, and the multi-channel data analysis method, the bubble entropy algorithm can more fully characterize the fault characteristics. Then, this method is combined with the machine learning algorithm to realize automatic fault diagnosis of rotating machinery. Multi-Cluster Feature Selection (MCFS) is used for feature selection to screen out useless features and improve feature extraction efficiency and feature quality. The MCFS method has excellent performance and does not need parameters, so it is suitable for adaptive fault diagnosis. Meanwhile, the EigenClass classifier using the concept of generalized eigenvalue is used to realize fault recognition. Besides, the recently proposed Gorilla Troops Optimizer (GTO) algorithm is used to improve EigenClass, and a GTO-optimized EigenClass (GTO-EigenClass) classifier is proposed to adaptively select parameters and achieve adaptive fault identification. The proposed fault diagnosis method of rotating machinery based on the improved bubble entropy is independent of data and application scenarios and is hardly affected by subjective factors. The ablation experiment proves the effectiveness of each improvement of bubble entropy. Also, the performance verification experiments and the comparative experiments prove that the method can effectively characterize different types of rotating machinery, different fault types, fault degrees, and composite faults, and it can achieve higher calculation efficiency and classification accuracy. When the sample length is 1024, the average accuracy of bearing and gearbox fault identification can reach 97.85 % and 97.57 % respectively, and when the sample length is 2048, the identification accuracy can reach 100 %.

Combining kernel principal component analysis and spatial group-wise enhance convolutional neural network for fault recognition of rolling element bearings

Data-driven deep learning methods have been widely used in the fault diagnosis of rolling bearings, while general network structures are complex with numerous parameters and computationally intensive calculations, leading to limited real-time performance and delayed fault detection. To address these challenges, this paper presents a novel hybrid framework, termed FKP-SGECNN, for efficient and accurate bearing fault identification. The proposed framework combines the strengths of kernel principal component analysis (KPCA), Fisher criterion, spatial group-wise enhance network (SGENet), and convolutional neural network. In the proposed framework, FKP incorporates Fisher criterion to optimize the kernel functions in KPCA, effectively reducing information redundancy in the input data. Furthermore, SGENet is integrated to streamline the network structure and enhance the model’s generalization capability, while maintaining high diagnostic accuracy. The performance of the hybrid framework implies a great potential, which was evaluated by several case studies using multi-class data of bearing faults.

A new fault diagnosis of rolling bearing based on phase-space reconstruction and convolutional neural network

PurposeTo solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction (PSR) theory and intelligent diagnosis techniques to extend the one-dimensional vibration signal to the high-dimensional phase space to reveal the system information implied in the univariate time series of the vibration signal.Design/methodology/approachIn this paper, a new method based on the PSR technique and convolutional neural network (CNN) is proposed. First, the delay time and the embedding dimension are determined by the C-C method and the false nearest neighbors method, respectively. Through the coordinate delay reconstruction method, the two-dimensional signal is constructed, and this information is saved in a set of gray images. Then, a simple and efficient convolutional network is proposed. Finally, the phase diagrams of different states are used as samples and input into a two-dimensional CNN for learning modeling to construct a PSR-CNN fault diagnosis model.FindingsThe proposed PSR-CNN model is tested on two data sets and compared with support vector machine (SVM), k-nearest neighbor (KNN) and Markov transition field methods, and the comparison results showed that the method proposed in this paper has higher accuracy and better generalization performance.Originality/valueThe method proposed in this paper provides a reliable solution in the field of rolling bearing fault diagnosis.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0113/

Unbalance Bearing Fault Identification Using Highly Accurate Hilbert–Huang Transform Approach

Abstract The dynamic characteristics of rolling element bearings are strongly related to their geometric and operating parameters, most importantly the bearing unbalance. Modern condition monitoring necessitates the use of intrinsic mode functions (IMFs) to diagnose unbalance bearing failure. This paper presents a Hilbert–Huang transform (HHT) method to diagnose the unbalanced rolling bearing faults of rotating machinery. To initially reduce the noise levels with slight signal distortion, the noises of the sample in normal and unbalanced fault states are measured and denoised using the wavelet threshold approach. The complex vibration signatures are decomposed into finite IMFs with ensemble empirical mode decomposition technique. Fast Fourier techniques are employed to extract the vibration responses of bearings that are artificially damaged using electrochemical machining on a newly established test setup for rotor disc bearings. The similarities between the information-contained marginal Hilbert spectra can be used to diagnose rotating machinery bearing faults. The data marginal Hilbert spectra of Mahalanobis and cosine index are compared to determine the fault indicator index’s similarity score. The HHT model’s simplicity enhanced the precision of diagnosis correlated to the results of the experiments with weak fault characteristic signals. The effectiveness of the proposed approach is evaluated with several theoretical models from the literature. The HHT approach is experimentally proven with unbalance diagnosis and capable of classifying marginal Hilbert spectra distribution. Because of its superior time-frequency characteristics and pattern identification of marginal Hilbert spectra and fault indicator indices, the newly stated HHT can process nonlinear, non-stationary, and even transient signals. The findings demonstrate that the suggested method is superior in terms of unbalance fault identification accuracy for monitoring the dynamic stability of industrial rotating machinery.

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

Trackside acoustic signals are useful for non-contact measurements as well as early warnings in the diagnosis of train wheelset bearing faults. However, there are two important problems when using roadside acoustic signals to diagnose wheel-to-wheel bearing faults; one is the presence of strong interference from strong noise and high harmonics in the signal, and the other is the low efficiency of bearing fault identification caused by it. Therefore, from the viewpoint of solving the two problems, a sparse operation method is proposed for denoising and detuning the modulation of the roadside acoustic signal, and a machine learning classifier with a Genetic Algorithm (GA)-optimized Radial Basis Neural Network (RBFNN) is proposed to improve the rate at which the features of roadside acoustic signal faults are recognized. Firstly, the background noise is filtered out from the Doppler-corrected acoustic signal using the Sparse Representation method, and the inverse wavelet transform is reconstructed into a noiseless signal. Secondly, the interference high-harmonic signal in the signal is filtered out using the Resonant Sparse Signal Decomposition (RSSD) method. Then, the GA is selected to optimize the parameters of the RBF neural network and build a fault diagnosis model. Finally, the extracted acoustic signal feature set is trained on the network model, and the trained model is used for testing. In summary, the sparse operation on the roadside acoustic signal processing and the GA-RBFNN diagnosis model were verified as being very effective in the diagnosis of roadside acoustic train wheel pair faults through the simulation experiment.

Comparison of Signal Processing Methods for Bearing Fault Detection

Abstract: Bearings with rolling elements are a typical part of rotating machinery. Vibration analysis is one of them that is frequently used to gauge the general wellbeing and condition of rotating machinery. Finding bearing defects is important in the pursuit of extremely reliable operations. The main relationship between vibration signal-based features and measured signals is that they can be used in order to assess the condition of a bearing. In order to examine the effectiveness of non-parametric harmonic approaches for defect identification using real motor data, the study recommends utilizing Thomson's Multitaper Estimation and Welch Periodogram Estimation. The researches show that this approach can identify outer race faults and inner race fault. The conclusive evidence points to Thomson's Multitaper Periodogram Estimation method as a successful technique for bearing fault identification.

Spectrum Shape Based Roller Bearing Fault Detection and Identification

This article describes a combination of numerical methods of automated fault detection and identification in rolling bearings, even when there is a limited knowledge about inspected bearings and their characteristic frequencies in particular. The most important feature is global approach to solving the stated problem using several rolling bearing diagnostic methods and techniques which work simultaneously and complement each other. These include the well-known Hilbert-based envelope, Discrete Wavelet Transform and Fourier Transform as well as new Outer Envelope Filtering, Pulse Train Scanning, spectrum shape analysis and novel application of known signal processing tools like Fuzzy Logic and Continuous Wavelet Transform. Combination of partial results obtained by each path allows for judging the probabilities of different fault types (outer-race, inner-race and roller). Two techniques additionally used here are spectrum scanning by a train of harmonic pulses (Pulse Train Scanning) and an outer envelope filter which removes high frequency noise, but enhances the most important short time diagnostic symptoms, which are often removed by standard filtering. A combination of such solutions allows for unambiguous and precise localization of characteristic fault frequencies in a spectrum without initial knowledge about their expected values.