Wheelset Bearing Research Articles

Adaptive extraction of characteristic ridges from time-frequency representation for wheelset bearings failure diagnosis under time-varying speed

Instantaneous frequencies-based order tracking is an excellent approach for bearing faults diagnosing at time-varying speeds. The key is to extract ridges from time-frequency representation. However, their precision seriously relies on the cost function and bandwidth selection. To address these issues, an adaptive extraction of characteristic ridges (AECR) method that integrates a variable-bandwidth ridge edge detection (VBRED) and an adaptive exponential cost function (AECF) is presented. VBRED provides a variable-bandwidth search area-based clustering algorithm to isolate neighboring components. AECF can be automatically adjusted based on the signal signature to ensure the smoothness of the ridge. The average frequency ratios of the extracted ridges are used to identify the health conditions of the bearings. Finally, the effectiveness of the proposed AECR is validated by simulations and experiments. The results prove that it performs excellently when the energy and relative position of the ridges are close, compared to other state-of-the-art methods.

Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine

This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might result in extensive periods of inactivity and maintenance, disrupting supply chains, increasing operational costs, and causing delays that affect both businesses and consumers. Fast fault identification is crucial for minimizing maintenance expenses. In this paper, we proposed a new integration of GWO for optimizing SVM hyperparameters, specifically tailored for handling sound-vibration signals in fault detection. We have developed a new fault detection method that efficiently processes fusion data and performs rapid analysis and prediction within 0.0027 milliseconds per data segment, achieving a test accuracy of 98.3%. Compared to the SVM and neural network models built in MATLAB, the proposed method demonstrates superior detection performance. Overall, the GWO-SVM-based method proposed in this study shows significant advantages in fault detection of wheelset bearing vibrations, providing an efficient and reliable solution that is expected to reduce maintenance costs and improve the operational efficiency and reliability of equipment.

Open set transfer learning for bearing defect recognition based on selective momentum contrast and dual adversarial structure

Data-driven rotating machinery fault diagnosis methods gradually enter people’s vision due to their fast and accuracy. However, in practical engineering applications, the unpredictability of machine failure modes and the appearance of new defect types can severely disrupt the diagnostic task with closed set defect recognition data-driven methods. Addressing this concern, this paper delves into the defect transfer diagnosis of rolling bearings in an open-set context and introduces the Momentum Contrastive Dual Adversarial (MCDA) domain adaptive method. This novel approach leverages instance-based difference comparison for unsupervised clustering and implements Negative Samples Selection Optimization (NSSO) via K Nearest Neighbor across all domains. It features intra-module adversarial learning for domain adaptation between source and target domains, and inter-module adversarial learning where encoders and feature extractors across different modules share weights. The methodology prioritizes the separation of private classes during the initial phase of contrastive learning, ensuring the isolation of target private classes. Concurrently, it aligns shared classes in the subsequent phase through semi-supervised training for domain adaptation. Our experiments on the Paderborn Dataset and Railway Freight Wheel-set Bearing (RFWB) Dataset demonstrate the effectiveness of this method.

A Graph Attention Based Multichannel Transfer Learning Network for Wheelset Bearing Fault Diagnosis With Nonshared Fault Classes

A Graph Attention Based Multichannel Transfer Learning Network for Wheelset Bearing Fault Diagnosis With Nonshared Fault Classes

DTMSgram: a novel optimal demodulation frequency band selection method for wheelset bearings fault diagnosis under wheel-rail excitation

Due to the influence of the wheel-rail excitation and complex transmission path, the fault signature of wheelset bearings is often obscured by complex background noise, which brings great challenges to the adaptive determination of the informative frequency band (IFB) in envelope analysis. In this paper, the vibration response characteristics of the axle box under wheel–rail excitation are revealed through full-scale bench tests. The experimental results show that tread damage will provoke periodic transient impacts and it has an obvious sparsity in the frequency domain. Inspired by this feature, a DTMSgram method is proposed to enhance the fault feature components of vibration signals through time-domain and frequency-domain noise reduction technology, and improve the accuracy of demodulation frequency band selection. Firstly, the amplitude spectrum of different weight coefficients is used to preprocess the vibration signal and adjust the full band component of the vibration signal. Then, the autocorrelation process is performed on each layer’s narrowband filtered signal envelope in kurtogram to further reduce noise interference from a time-domain perspective. Moreover, a two-dimensional color map is constructed showing the normalized squared envelope spectrum of the IFBs determined simultaneously from the modified signal. Finally, the effectiveness of the proposed method is verified by simulated signal and experimental data of three sets of full-size wheelset bearing systems. The analysis results indicate this proposed method can effectively overcome the influence of complex wheel–rail excitation interference and can diagnose multi-source faults simultaneously.

Multirotational Speed Data Augmentation and Data Repair of High-Speed Train Wheelset Bearings Using Graph Speed Classifier GAN

Multirotational Speed Data Augmentation and Data Repair of High-Speed Train Wheelset Bearings Using Graph Speed Classifier GAN

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.

Manifold-Contrastive Broad Learning System for Wheelset Bearing Fault Diagnosis

Newly deployed trains have massive normal data and scarce faulty data for training, which limits the diagnosis accuracy with class imbalance problem of small samples. Considering that there are a lot unutilized information hidden in the abundant unlabeled monitoring data, this paper proposes a novel method named manifold-contrastive broad learning system, which utilizes the online updating approach for dealing with the class imbalance problem of small samples. This method constructs a novel one-class broad-learning classifier based on an inherency-guided comparison mechanism, which can classify and annotate unlabeled data online. This classifier employs contrastive manifold matrices to maintain the inherent structures, which is not affected to the overfitting caused by imbalanced samples. Secondly, inspired by the active learning, this classifier proposes the minimum-error strategy to annotate the samples by classifying the modes, which solves the problem of insufficient training data. Thirdly, this method applies an incremental learning strategy that continuously absorbs the newly annotated data to update the model online, which improves the model accuracy under the data imbalanced condition. Finally, the feasibility and effectiveness of the proposed method are verified by wheelset bearing data collected from a test rig of a Chinese rolling stock company.

Scale-independent shrinkage broad learning system for wheelset bearing anomaly detection under variable conditions

Wheelset bearing fault diagnosis is essential to guarantee safe rail system operation. However, real-time monitoring signals of high-speed trains are nonstationary under variable conditions. Additionally, wheelset bearings operate in a normal state most of the time, and therefore, only normal data are available for training in practice. Under such a scenario of variable operating conditions and lacking labelled data, it is still necessary to timely detect the possible abnormal states of wheelset bearings. Currently, one-class classification is a suitable approach for real-time anomaly detection, but its accuracy is affected by the linear relationship of network nodes. Concerning this issue, this paper presents a novel method named scale-independent shrinkage broad learning to detect the abnormal states of wheelset bearings in real time. First, scaled rotation regularization is presented to convert the output constraint into the node distance constraint. Then, the variance-guided mapping mechanism is constructed to solve the linear relationship between nodes. Meanwhile, the optimal network mapping parameters are generated iteratively according to a so-called high-polymerization strategy, with which new output weights are formed after the feedback calculation. This method eliminates the linear correlation of node features during the iteration and realizes one-class classification detection of abnormal states. Finally, wheelset bearing data collected from a test rig of a Chinese rolling stock company are utilized to demonstrate the feasibility and effectiveness of the proposed approach in variable conditions.

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.

TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis

Wheelset bearing is a typical vulnerable structural component in high-speed trains and heavy haul vehicles. In addition to the typical nonlinear and nonstationary characteristics, the vibration signal of wheelset bearing also contains track subgrade vibration and transmission path coupling interference components. To solve this problem, this paper proposes a new feature extraction method for wheelset bearing faults. This method constructs the Teager energy spectrum correlation kurtosis, which is purposely sensitive to periodic fault impulse components, as the objective function. The Q-factor and redundancy of tunable Q-factor wavelet transform are selected by using the parameter convex optimization method, which makes the signal decomposition have better sparsity, so as to extract fault information accurately. Simulated analysis, experimental signal analysis of QPZZ-II test-bed, and experimental signal analysis of wheelset bearing test-bed show that the proposed method can suppress the influence of nonperiodic transient impulse components, harmonic components, and noise components in the signal and accurately extract the periodic impact characteristics of bearings.

A Simulation Investigation on the Dynamic Behavior of Wheelset Bearings Caused by Wheel Diameter Difference

AbstractA bearing‐vehicle‐track coupling dynamic model is built in this paper and the validity of the model is proved by comparing the simulation data with the theoretical data. Based on this model, the influence of wheel diameter difference (WDD) on the dynamic behavior of wheelset bearing is studied. The results demonstrate that when the bogie has a rear WDD, lateral displacement (LaD) of the front wheelset is the smallest. When the bogie has an anti‐phase WDD, LaD of the rear wheelset is the smallest, while when the bogie has an in‐phase WDD, LaD of the front and rear wheelsets is the largest, and LaD of the front and rear WDD is between the two, which is in line with the theoretical analysis results. Compared with LaD results of the wheelset bearing outer raceway under normal conditions, when the wheel has a diameter difference, LaD distance of the outer race increases with the WDD increasing, and the frequency of lateral movement also increases with the increase against the WDD gradation. The high‐frequency and large‐distance lateral movement of the wheelset bearing outer race under the WDD will increase the roller‐outer raceway force, and accelerate the failure of the bearing.

A recursive multi-head graph attention residual network for high-speed train wheelset bearing fault diagnosis

Wheelset bearings are a core component of high-speed trains, and their fault diagnosis is the key to smooth operation. Deep learning is widely used in fault diagnosis due to its powerful classification ability. To explicitly fit the features of vibration signals and further explore the relationship between the signals, the graph attention network (GAT) is becoming a focus of research. Unlike traditional graph neural networks, GATs can focus on edges with stronger correlations with vertices, making the model more powerful when fitting graph samples in non-Euclidean space. However, existing GATs have two limitations. Firstly, most graph construction methods do not consider the characteristics of vibration signals, so the graph interpretation is not good. Secondly, the existing methods of graph attention coefficient cannot effectively reflect the importance of edges. To address these issues, a recursive multi-head graph attention residual network (RMHGARN) is proposed. In RMHGARN, vibration signals are transformed into recurrence graphs due to the recursive nature of nonlinear time series. A multi-kernel Gaussian symmetric graph attention mechanism is proposed to obtain the Hilbert spatial distribution between neighboring vertices. In addition, a graph encoding module is proposed to improve the feature representation of input samples. The effectiveness and superiority of RMHGARN under strong noise samples are verified using three wheelset bearing datasets.

Significance Support Vector Machine for High-Speed Train Bearing Fault Diagnosis

Wheelset bearing is the most critical component in High-Speed Train (HST) and crucial for HST safe and efficient operation. As a wide applying method for bearing health monitoring, Support Vector Machine (SVM) may easily lead to overfitting due to giving the same attention to all samples especially when training dataset contains outliers. For measuring the sample importance corresponding with normal or outlier in feature space of SVM training process, sample significance is represented by utilizing sample spatial aggregation information of feature space in which the normal samples having a good aggregation and outliers caused by noise or uncertainty tending to deviate from the sample aggregation group. Then by introducing sample significance, an improved Significance SVM (SSVM) is proposed for enhancing the robustness of SVM with noisy samples or uncertainty. SSVM overcomes the drawback of SVM and gives outlier samples less attention during model training by assigning significant coefficients to samples in the model training process. Combing SSVM, multi-domain features and feature selection, a noise robustness fault diagnosis method is successfully applied on HST bearing vibration dataset. The experiment results verify the effectiveness and the stability of the proposed method under different noise levels.

Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains

High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness.

A Multichannel MN-GCN for Wheelset-Bearing System Fault Diagnosis

The working condition of high-speed train wheelset bearings is complex and bad, which makes its vibration signal contain strong noise. To obtain more information of samples, signals are constructed into graphs and fit by graph convolution network (GCN). The existing GCN-based models still have some problems, such as insufficient theoretical basis of graph and the unstable and overfitting during training of models, which significantly limits the diagnosis accuracy. To address these issues, first, a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -recurrence network is proposed to construct the recurrence graph (RG). The recurrence relationship between samples is analyzed by the recursive nature of vibration signals. Maximum mean discrepancy (MMD) is used to obtain the regenerated Hilbert spatial distribution distance between two samples to reduce the weight gap between different recursive pairs. Also, the proposed method has better ex ante interpretability. Second, a vertex mean normalization (MN) layer is proposed to normalize individual vertex features to introduce some spatial perturbations and reduce the variance of vertex features, which can improve training stability and reduce overfitting. In addition, multichannel MN-GCN is proposed to fit multiscale graph features. Analyze recurrence nature of samples at different scales through RGs with varying densities of connection to improve the antinoise ability. Experimental results show that the proposed model can obtain spatial topology information accurately and improve the wheelset-bearing system fault diagnosis performance.

Multioperator Morphological Undecimated Wavelet for Wheelset Bearing Compound Fault Detection

Wheelset bearing compound faults are observed as impulses in the vibration measurements but immersed in noise. The morphological undecimated wavelet (MUW) is an effective tool for recovering fault-related impulses from vibration mixtures. To date, the reported MUWs adopt an identical morphological operator in each level of decomposition without exception. However, effectively capturing signal signatures by repeatedly using a noise elimination operator, an impulse extraction operator, or even the product of two operators is unattainable. Spurred by this deficiency, in this paper, we propose a multi-operator MUW (MOMUW). A three-level structure: noise reduction, impulse extraction, and further denoising and feature enhancement, is developed in the MOMUW, and different morphological operators are designed for each decomposition level to more purposefully denoise and extract impulse features. The developed MOMUW is applied to measured wheelset bearing vibration data, with the results demonstrating that it can accurately detect wheelset bearing compound faults. Compared with the reported MUWs, the proposed approach presents superior performance. Furthermore, MUWs with varying operators and levels are analyzed and compared. Their success and failure in bearing fault diagnosis are interpreted and discussed, laying a theoretical foundation for constructing new MUWs.

Fault detection and analysis for wheelset bearings via improved explicit shift-invariant dictionary learning

The wheelset bearing is an indispensable part of the high-speed train, and monitoring its service performance is a concern of many researchers. Effective extraction of those impulse signals induced by the defects on the bearing elements is the key to fault detection and behaviour analysis. However, the presence of considerable noise and irrelevant components brings difficulties to extracting the wheelset bearing fault impulse signals from the measured vibration signals. This paper proposes an improved explicit shift-invariant dictionary learning (IE-SIDL) method to address this issue. Based on the shift-invariant characteristics of the wheelset bearing fault impulse signal in the time-domain, the circulant matrix is used to construct a shift-invariant dictionary and explicitly characterize the fault impulses at any time. To improve the efficiency of dictionary learning, a method of three flips is introduced to realize fast dictionary construction, and the frequency-domain reconstruction property of the circulant matrix is employed to quickly update the dictionary. Besides, an indicator-guided subspace pursuit (SP) method based on the sparsity of envelope spectrum (SES) is adopted for the sparse coding to improve sparse solution accuracy and adaptation. The effectiveness of the IE-SIDL method is proved through the simulated and experimental signals. The results demonstrate that the improved dictionary learning method has an excellent capacity in extracting fault impulse signal of the wheelset bearings, and the good time- and frequency-domain characteristics of the processed signals facilitate fault detection and behaviour analysis.

The Msegram: A useful multichannel feature synchronous extraction tool for detecting rolling bearing faults

Multichannel signals collected by multiple sensors contain richer condition information of equipment than single-channel signals. However, such issues as simultaneous denoising, adaptive decomposition and synchronous extraction are still challenging for multichannel signals, which are beneficial to accurate fault diagnosis. Thus, a useful multichannel feature synchronous extraction tool is proposed for detecting rolling bearing faults, named as Msegram. First, a tensor synchronization denoising method based on high order singular value decomposition (HOSVD) is proposed for multichannel signal preprocessing. Original multichannel signals of testing bearings are constructed to be a third-order tensor by phase space reconstruction. Hereinto, a singular entropy increment is adopted to determine a reasonable singular order for each unfolding, and an optimal core tensor is obtained for local reconstruction analysis. Second, multi-layer K-value multivariate variational mode decomposition (MVMD) is designed after the multichannel noise reduction to realize synchronous adaptive filtering and decomposition for the multichannel signals. Third, inspired by the idea of the spectral kurtosis, a tower-shaped crest factor of envelope spectrum (EC) diagram similar to Fast Kurtogram (FK) is proposed to visualize the output of multichannel bearing fault feature results. According to the tower-shaped EC diagram with the maximum fault crest factor, the optimal analytic results of multichannel signals are selected and output to synchronously extract bearing fault features. Finally, repeatable simulations and two experimental fault cases of rolling bearings are implemented to demonstrate the practicability and effectiveness of the proposed method. The results show that the proposed method can successfully reveal the compound faults from experimental bearing and effectively identify the compound faults from locomotive wheelset bearing.

Experience of operation of the UDP-2001SM railway wheelset bearing vibration diagnostics installation and the OMSD-02 mechanism diagnostics system

The Aim of the paper is to compare the UDP-2001SM installation for vibration diagnostics of bearings of freight, refrigerator and passenger car wheels and the OMSD-02 mechanism diagnostics system that since recently has been used in the Belarusian Railway.Methods. By way of analysis of the UDP-2001SM vibration diagnostics installation and the OMSD-02 mechanism diagnostics system, the authors considered the list of identifiable defects, method for defining threshold values (reference levels) of vibroacoustic pulses and noises for the criteria of “faulty” and “good”, statistical data for rejected axleboxes of freight and passenger car wheelsets in respect to the overall number of conducted measurements, the experience of operation in various car shops of the Belarusian Railway. Results. The paper describes the process of vibration diagnostics of wheelset bearings of freight and passenger cars using the case of stationary acquisition of vibroacoustic signals from acoustic plates that interact with the diagnosed axlebox. The authors present the diagrams of the UDP-2001SM installation and OMSD-02 system, as well as the defects of bearings that are to be identified using the above diagnostics equipment. Conclusion. The paper showed the advantages and disadvantages of the above diagnostics systems based on the experience of their application in a number of car shops of the Belarusian Railway.