Bearing Fault Diagnosis Approach Research Articles

Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.

A bearing fault diagnosis approach based on an improved neural network combined with transfer learning

Abstract In modern industrial systems, bearing failures account for 30%–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.

Cross-domain bearing fault diagnosis using dual-path convolutional neural networks and multi-parallel graph convolutional networks

Bearing fault diagnosis is significant in ensuring large machinery and equipment's safe and stable operation. However, inconsistent operating environments can lead to data distribution differences between source and target domains. As a result, models trained solely on source-domain data may not perform well when applied to the target domain, especially when the target-domain data is unlabeled. Existing approaches focus on improving domain adaptive methods for effective transfer learning but neglect the importance of extracting comprehensive feature information. To tackle this challenge, we present a bearing fault diagnosis approach using dual-path convolutional neural networks (CNNs) and multi-parallel graph convolutional networks (GCNs), called DPC-MGCN, which can be applied to variable working conditions. To obtain complete feature information, DPC-MGCN leverages dual-path CNNs to extract local and global features from vibration signals in both the source and target domains. The attention mechanism is subsequently applied to identify crucial features, which are converted into adjacency matrices. Multi-parallel GCNs are then employed to further explore the structural information among these features. To minimize the distribution differences between the two domains, we incorporate the multi-kernel maximum mean discrepancy (MK-MMD) domain adaptation method. By applying the DPC-MGCN approach for diagnosing bearing faults under diverse working conditions and comparing it with other methods, we demonstrate its superior performance on various datasets.

Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features.

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network

The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy.

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimodal fusion (LMF) to effectively handle the signals of acoustic–vibration fusion. By combining these techniques, the aim is to improve the accuracy and reliability of rolling bearing fault diagnosis. Firstly, improved EEMD combined with GS-SVR and a window function is used for rolling bearing vibration signal decomposition. Singular value methods are used to filter and reconstruct the results. Secondly, Markov transition fields (MTFs) are used to encode vibration signals into 2D images. LMF is used for the fusion of vibration and sound signals. An improved Squeeze and Excitation ResNet50 network is proposed for feature identification and classification of rolling bearing composite fault data. Finally, the method undergoes rigorous testing and evaluation using rolling bearing data. The experimental outcomes demonstrate that, in comparison to traditional neural networks, the enhanced SE-ResNet, integrated with GS-SVR-EEMD and LMF, attains superior diagnostic accuracy. Additionally, the proposed approach can be effectively utilized for diagnosing rolling bearing composite faults.

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

This study presents a digital twin (DT) based wind turbine bearing fault diagnosis approach to address the issues of insufficient fault sample size and inaccurate diagnosis. To assist in diagnosing bearing faults in wind turbines, a DT system was built. Bearing vibration signal enhancement processing, which is based on the Hilbert-Huang transform, is used to improve the data samples of vibration signals and decrease the noise in these signals. In order to diagnose bearing defects in wind turbines, a convolutional neural network model was trained and tested using data-enhanced samples. The experimental results showed that the suggested method is feasible and effective, increased the stability and accuracy of defect diagnosis in wind turbine bearings, and solved the problem of data augmentation in one-dimensional vibration signals.

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method.

Sparse Bayesian Learning Approach for Compound Bearing Fault Diagnosis

Compound bearing fault diagnosis is an essentially challenging task due to the mutual interference among multiple fault components. The state-of-the-art methods usually take the potential fault characteristic frequencies as the prior knowledge and then try to recover every fault component by exploiting the impulse signal sparsity. However, they inevitably suffer from algorithmic degradation caused by energy leakage, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm approximation, and/or improper parameter selection. To handle these shortcomings, in this paper, we propose a novel sparse Bayesian learning (SBL)-based method for the compound bearing fault diagnosis. We first present a new categorical probabilistic model to efficiently capture the truly-occurred fault components with a truncated feasible domain, which can greatly reduce the energy leakage effect. Then, we devise a more general SBL framework to recover the compound sparse impulse signal under the new categorical probabilistic model. The newly proposed method successfully avoids the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm approximation and manual parameter selection, thus it can yield much higher accuracy and robustness. Both simulations and experiments demonstrate the superiority of the developed method.

Adaptive bearing fault diagnosis using reference frequency and modified scalogram

Previous studies on the diagnosis of natural faults in rolling element bearings have primarily concentrated on detecting an increase in the frequency amplitude across a broad frequency spectrum within the measured signal when a fault occurs in the bearing. However, this study introduces an adaptive method centered on the conspicuous distinction in frequency amplitude between the shaft and bearing fault frequencies as the bearing fault progresses. Specifically, we propose a bearing fault diagnosis approach leveraging the shaft frequency amplitude as a constant reference independent of prevailing conditions, as it consistently maintains a relatively steady level regardless of the presence of bearing faults. Moreover, we devised a modified scalogram to enhance the diagnostic performance for bearing faults by consistently and prominently visualizing information pertinent to faults. We comprehensively assessed fault diagnosis accuracy between a convolutional neural network employing the scalogram and one employing the modified scalogram to verify the diagnostic performance of the adaptive method. This analysis yielded accuracies of 93.6% and 99.3%, respectively. Furthermore, the relationship between the bearing fault diagnosis results and the visualized fault-related information in the scalogram and modified scalogram was analyzed using gradient-weighted class activation mapping, providing evidence supporting improved fault diagnosis performance using the adaptive method.

CGASNet: A Generalized Zero-Shot Learning Compound Fault Diagnosis Approach for Bearings

CGASNet: A Generalized Zero-Shot Learning Compound Fault Diagnosis Approach for Bearings

Cross-domain fault diagnosis method for rolling bearings based on contrastive universal domain adaptation

To address the unknown spatial relationship between source and target domain labels, which leads to poor fault diagnosis accuracy, a contrastive universal domain adaptation model and rolling bearing fault diagnosis approach are proposed. The approach introduces bootstrap your own latent network to mine the data-specific structure of the target domain and proposes rejecting unknown class samples using an entropy separation strategy. Simultaneously, a source class weighting mechanism is designed to improve the transferable semantics augmentation method by assigning various class-level weights to source categories, which improves the alignment of the feature distributions in the shared label space to further construct fault diagnosis models. Experimental validation on two rolling bearing datasets confirmed the superior fault diagnosis accuracy of the proposed method under diverse working conditions.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis

The efficient diagnosis of bearing faults requires the extraction of informative features. This paper presents a novel approach that combines Weighted Principal Component Analysis (WPCA) with the Gaussian Mixture Model (GMM) for bearing fault diagnosis. The method employs GMM as a fault classifier, aiming to enhance both efficiency and diagnostic accuracy. The proposed algorithm, Expectation Selection Maximization (ESM), introduces a feature selection step to identify the most relevant features for effective bearing fault detection. Specifically, the suggested algorithm utilizes the conditional entropy divergence indicator, a statistical metric, to quantify the significance of features in detecting bearing faults. To validate the effectiveness of this approach, two distinct case studies are conducted using datasets obtained from the University of Ottawa and Case Western Reserve University (CWRU). These datasets encompass a wide range of bearing working conditions, providing a comprehensive evaluation. Experimental results underscore the merits of the approach, achieving an average accuracy rate of 93% for the University of Ottawa dataset and 80% for the CWRU dataset. Furthermore, the findings highlight the superior performance of the proposed method compared to alternative techniques, as evidenced by the receiver operating characteristic (ROC) curve metric.

Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments

Due to rolling bearings usually operate under fluctuating working conditions in practical engineering, the raw vibration signals generated by bearing faults have nonlinear and non-stationary characteristics. Additionally, there is a lot of noise interference in the collected bearing vibration signal, which indicates that it is difficult to extract bearing fault information and obtain a satisfactory diagnosis accuracy via using traditional method. Deep learning provides a shining road to address this issue. Nevertheless, traditional deep network model has the shortcomings of poor generalization performance and weak robustness in the feature learning. To improve fault recognition accuracy and obtain a favorable anti-noise robustness, this paper proposes a novel bearing fault diagnosis approach based on attention mechanism-guided residual convolutional variational autoencoder (AM-RCVAE). Firstly, the improved residual module is constructed to overcome the convergence difficulty problem caused by network degradation and promote the model generalization performance by replacing the batch normalization (BN) layer in the traditional residual module with the adaptive BN layer. Subsequently, by incorporating the convolutional block attention module and the improved residual module into convolutional variational autoencoder, a deep network model termed as AM-RCVAE is presented to automatically learn fault features from the original data and perform fault diagnosis tasks. The effectiveness of the proposed approach is verified via two experimental cases. Moreover, the recognition accuracy and diagnostic performance of the proposed approach have been certain improved compared with several representative methods.

A New Bearing Fault Diagnosis Approach based on Common Spatial Pattern Features

This study offers a novel intelligent detection method of rolling bearings that applied: the Common Spatial Pattern (CSP) method for feature extraction of bearing faults. CSP maximizes the variance ratio of the two class signal matrices coming from different sources. Different from the frequency features, characteristic vibration features from each measurement that can distinguish between the faulty and the healthy bearings were extracted. Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbour (k-NN) methods were tested on two different data sets. An 88.5% accuracy was obtained with ANN for two-class fault detection and 93.5% for fault classification. The classification results were compared with classical time domain feature sets. CSP features have achieved higher accuracies in all two-class and multiclass cases. Therefore, the CSP features can be a possible means for condition monitoring of bearings through vibration data.

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Accurately identifying the health status of train running gear bearings is crucial to ensure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effectively filter out modal components containing rich fault information than the comparison methods.

Attention-Based Bilinear Feature Fusion Method for Bearing Fault Diagnosis

The bearing is the key component of rotating mechanical equipment, so the fault diagnosis of bearings is important to improve the reliability and safety of equipment operation. In recent years, feature fusion method has been extensively explored in the health monitoring and fault diagnosis of bearings. However, almost all the existing feature-fusion-based fault diagnosis methods extract features from different signals independently and concatenating them simply. It will lead to the failure of achieving the expected diagnostic accuracy because the complementary fault information is not fully mined and fused. This article proposes a novel bearing fault diagnosis approach based on mutual attention and bilinear model to address these issues. The features extracted from different input are interactive through mutual attention and are fused by the bilinear model, so the complementary fault features are effectively extracted and fine-grained fused. Experiments are conducted on the Paderborn bearing dataset to verify the effectiveness of the proposed method. Results show that the proposed method can effectively extract complementary fault features from different signals and deeply fuse them, and its diagnosis accuracy is up to 99.86%. Its performance is much better than that of simple concatenation and the feature fusion methods proposed in the reference.

Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting

A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes a feature extraction module, and uses a deep convolutional neural network to map bearing samples to a high-dimensional feature space. Secondly, uses the transfer learning concept to design a weighted domain discriminator, and adaptively weights the samples; and finally, through the confrontation in the feature space, the bearing samples are classified. Training to increase the domain similarity of the healthy state samples shared by the target domain and the source domain. Then measuring the similarity between these samples based on the sample weight size, and setting a threshold to label the additional fault state samples of the target domains as unknown. The suggested technique is validated using gearbox bearing data, roller bearing data from Case Western Reserve University, and locomotive wheel bearing data. The diagnostic accuracy of the samples is less than 80%, suggesting that the suggested approach can successfully overcome the effects of extra fault state samples and diagnose roller bearing faults.

A variable-speed-condition bearing fault diagnosis methodology with recurrence plot coding and MobileNet-v3 model.

To improve the quality of the non-stationary vibration features and the performance of the variable-speed-condition fault diagnosis, this paper proposed a bearing fault diagnosis approach with Recurrence Plot (RP) coding and a MobileNet-v3 model. 3500 RP images with seven fault modes were obtained with angular domain resampling technology and RP coding and were input into the MobileNet-v3 model for bearing fault diagnosis. Additionally, we performed a bearing vibration experiment to verify the effectiveness of the proposed method. The results show that the RP image coding method with 99.99% test accuracy is superior to the other three image coding methods such as Gramian Angular Difference Fields, Gramian Angular Summation Fields, and Markov Transition Fields with 96.88%, 90.20%, and 72.51%, indicating that the RP image coding method is more suitable for characterizing variable-speed fault features. Compared with four diagnosis methods such as MobileNet-v3 (small), MobileNet-v3 (large), ResNet-18, and DenseNet121, and two state-of-the-art approaches such as Symmetrized Dot Pattern and Deep Convolutional Neural Networks, RP and Convolutional Neural Networks, it is found that the proposed RP+MobileNet-v3 model has the best performance in all aspects with diagnosis accuracy, parameter numbers, and Graphics Processing Unit usage, overcoming the over-fitting phenomenon and increasing the anti-noise performance. It is concluded that the proposed RP+MobileNet-v3 model has a higher diagnostic accuracy with fewer parameters and is a lighter model.