Rolling Bearing Fault Diagnosis Research Articles

Parametric-margin projection twin support matrix machine and its application in fault diagnosis of roller bearing

Parametric-margin projection twin support matrix machine and its application in fault diagnosis of roller bearing

Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors

Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors

Reinforcement Learning for Rolling Bearing Fault Diagnosis—A Comprehensive Review

Reinforcement Learning for Rolling Bearing Fault Diagnosis—A Comprehensive Review

Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance.

Shuffle-fusion pyramid network for bearing fault diagnosis under noisy environments

Abstract Recent advancements in deep learning have propelled the exploration of big data-driven fault diagnosis techniques. Nevertheless, traditional models often suffer from prohibitive computational demands, rendering them impractical for on-site deployment in rolling bearing fault diagnosis. To address this challenge, this paper introduces a novel lightweight fault diagnosis model with the pyramid architecture, named Shuffle-Fusion Pyramid Network (Shuffle-FPN). The model heralds several innovations: (1) A pyramid structure is designed to amalgamate fault signals across various scales, enlarging the network’s breadth and curtailing its depth. (2) Depth wise separable convolutions are adopted to streamline network parameters, thus achieving a lightweight model, and channel shuffling to ensure thorough information fusion across convolutional channels. (3) A global representation module is employed to offset the loss of global context, which accompanies increased convolutional depth. Collectively, these innovations enable Shuffle-FPN to extract nuanced fault features amidst noise and to operate on devices with limited memory, ensuring real-time fault diagnosis even in complex environments. Rigorous experiments on public datasets from Paderborn University (PU), supplemented by validations with our research group’s experiment data, reveal that Shuffle-FPN excels in fault identification under severe noise conditions and reduces memory footprint, which solidifies the superiority of Shuffle-FPN for practical fault diagnosis.

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Research on a self-powered rolling bearing fault diagnosis method with a piezoelectric generator for self-sensing

The self-powered fault diagnosis methods provide a new idea to realize fault diagnosis without using wired sensors or battery-supported sensors. However, current self-powered fault diagnosis methods mainly depend on self-powered sensors, most of which may destroy the original structure of mechanical parts or contain connected energy harvesters and sensors, resulting in reduced reliability and stability. In this paper, a self-powered rolling bearing fault diagnosis method with a piezoelectric generator (PEG) for self-sensing is proposed. The PEG can be simply installed without destroying the bearing structure, and its electrical outputs excited by the mechanical vibrations can be further used for fault diagnosis. The energy conversion mechanism of a conventional monostable PEG and the basic knowledge of the convolutional neural network (CNN) model are first introduced, based on which the self-powered fault diagnosis approach is proposed. Experimental work is conducted to identify the parameters of the PEG's governing equation and verify its effectiveness. The responses of a PEG installed onto a rolling bearing experimental table are further measured and analyzed. Finally, the CNN model is trained based on the electrical outputs of the PEG under different installation positions, different rotation speeds and different healthy statuses of the bearings. The research results show that the average classification accuracies of this method for rotational speeds of 1200 rpm, 3000 rpm and 4800 rpm can achieve 88.389%, 98.99% and 98.431%, respectively, which are quite feasible in practical applications. This work shows a totally new idea of adopting a vibration energy harvester as a self-sensing device to achieve self-powered fault diagnosis, which has wide application potential in the future.

Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment

To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis.

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.

Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.

Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method’s average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized.

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

Research on rolling bearing fault diagnosis technology based on singular value decomposition

To solve the difficulty of selecting the number of effective singular values in Singular Value Decomposition denoising, a new method to determine the number of effective singular values is proposed. The proposed method to determine the number of effective singular values is based on the non-zero singular value distribution law of the Hankel matrix constructed by the signal. Specifically, the number of effective singular values in the Hankel matrix is twice the number of frequencies contained in the signal, and the difference between the effective singular values of the noisy signal and the non-zero singular values of the pure signal is very small. The proposed method for determining the number of effective singular values is to perform differential processing on the singular values of the signal and normalize the difference obtained. An empirical parameter T is provided, and the number of effective singular values is determined by comparing them with the normalized results. The proposed method is applied to the simulated and measured rolling bearing signals, and the results are compared with the wavelet threshold denoising method. The results show that the proposed method for determining the number of singular values can effectively filter out the noise frequency contained in the signal while maintaining the characteristic frequency of the signal and achieving the purpose of mechanical equipment fault diagnosis.

Digital twin-driven attention-guided convolutional networks for intelligent fault diagnosis across different domains

Fault diagnosis of rolling bearings is crucial in industries to ensure the reliability, safety, and economic feasibility of mechanical systems. Conventional data-driven methods for fault diagnosis depend on pre-existing datasets containing various failure modes for training. However, obtaining such datasets can be challenging, especially in critical industrial environments, hindering the applicability of these methods across different scenarios. To address this challenge, this research introduces a novel approach called a digital twin-driven attention-guided convolutional network (DTACN) for bearing fault diagnostics with limited data. This study offers two significant enhancements: (1) Development of an intricate digital twin model for bearings, which includes multi-scale Kinetic simulations of the bearing’s operational parameters. This model utilizes the architectural characteristics of the bearing and the severity of the failure to predict the vibratory system’s response accurately; and (2) Establishment of a domain adaptation-based framework employing deep convolutional models and attention mechanism to transfer knowledge from simulated signals to real signals. This framework facilitates efficient fault recognition even in scenarios with limited prior knowledge. The effectiveness of the proposed approach has been rigorously validated through comprehensive experiments.

Fault diagnosis of railway freight car rolling bearings based on ACMD and improved MCKD

To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions, this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition (ACMD) and an optimized Maximum Correlation Kurtosis Deconvolution (MCKD) using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation (SCSSA). Firstly, ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions (IMFs). Then, the IMFs are filtered according to the Gini coefficient indicator, with the IMF having the largest Gini coefficient selected as the optimal component. Secondly, the SCSSA is employed to iteratively optimize the filter length L, fault signal period T, and displacement parameter M in the MCKD algorithm, determining the optimal parameter combination for MCKD. This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis. The optimized MCKD is then applied to the optimal component, and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum. To verify the effectiveness and generalizability of the proposed method, simulations, experimental signals from the Case Western Reserve University Bearing Center, and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults. The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

MARNet: Multi-head attention residual network for rolling bearing fault diagnosis under noisy condition

Rolling bearings are crucial components of rotating machinery, and their health states directly affect the overall performance of the machinery. Therefore, it is exceedingly necessary to detect and diagnose bearing faults. Numerous bearing fault diagnosis methods have been successfully used for ensuring the safe operation of rotating machinery. However, in practical working environments, there is a considerable amount of noise, resulting in traditional methods incapable of achieving accurate fault diagnosis. This paper proposes a new multi-head attention residual network (MARNet) for rolling bearing fault diagnosis under noisy condition. MARNet optimizes residual units by simplifying multi-layer convolutions into a single-layer convolution and replaces the rectified linear unit (ReLU) function with the exponential linear unit (ELU) function to obtain a more appropriate activation function. Additionally, the multi-head attention mechanism is introduced into the residual block to capture correlation information between any two time sequences, enhancing the network’s feature extraction capability. The effectiveness and superiority of the MARNet in noisy environments are demonstrated through conducting the two bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU). The experiment results show that the proposed method exhibits anti-noise characteristics and generalization capability compared with several up-to-date deep learning methods for fault diagnosis of rolling bearings.

A coarse and fine-grained deep multi view subspace clustering method for unsupervised fault diagnosis of rolling bearings

Abstract To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

Fault diagnosis algorithm based on GADF-DFT and multi-kernel domain coordinated adaptive network

To address the problems of low detection accuracy of rolling bearings under different loads and the difficulty of effectively identifying the lack of labelled data, a rolling bearing fault diagnosis method combining GADF-DFT image coding and Multi-kernel domain coordinated adaptation network is proposed. Firstly, the vibration signal is converted into a two-dimensional image using GADF coding technology, and then the GADF image is converted into the frequency domain using discrete Fourier transform to extract deeper feature information. Combined with the multi-source domain adaptive method, the public feature extraction module is used to initially achieve feature mining of the image; the MK-MMD algorithm of the domain-specific adaptive module reduces the difference in feature distribution between the source and target domains; and the final classification difference minimization module reduces the problems caused by the classification errors that may be generated by the different domain classifiers due to the fact that the data samples are located near the category boundaries. The test uses the Case Western Reserve University dataset and divides the dataset with different operating conditions as the source and target domains, and the test results show that the proposed model demonstrates its effectiveness in responding to the complex operating condition changes in rolling bearing fault detection in multiple operating condition migration tasks, good adaptability and robustness, and is able to achieve accurate fault diagnosis under different operating conditions.