Bearing Fault Diagnosis Model Research Articles

A Bearing Fault Diagnosis Model with Convolutional Cross Transformer and ResNet18

Abstract In the industrial field, malfunction of rotating machinery, especially bearings, can cause significant economic losses to enterprises. Addressing the limitations of traditional fault diagnosis methods, such as poor generalization performance and low noise resistance, this paper introduces a fault diagnosis model that parallels the Cross Convolutional Transformer and ResNet18 (CCTAR). The proposed CCTAR utilizes two feature extraction channels, aimed at balancing the extraction of local and global features, and the specially designed convolutional cross-decoding layer has excellent noise resistance, surpassing traditional multi-layer Transformer encoding layers with a single-layer structure. CCTAR achieves commendable recognition accuracy across multiple datasets and maintains high accuracy in noisy environments. Furthermore, transfer learning experiments have demonstrated the proposed model's capability to achieve superior fault diagnosis performance across different working conditions with a limited number of samples, highlighting its practical significance.

An Improved Rolling Bearing Fault Diagnosis Model of Long Short-Term Memory Network Based on VMD Denoised Vibration Signals

Data driven fault diagnosis methods are increasingly being used for condition monitoring of rotating machinery in the era of Industry 4.0. The effectiveness of Variational Mode Decomposition (VMD) denoised vibration signals in improving the Long Short-Term Memory (LSTM) method for the intelligent fault diagnosis of a rolling bearing is reported. The raw and VMD denoised vibration signals of rolling bearings are provided as inputs to the LSTM network for classification of bearing condition. The efficacy of the methodology to extract the fault information is assessed through datasets obtained from experiment test rig and through the open-source dataset of Case Western Reserve University (CWRU). A comparative analysis is also carried out using both VMD based decomposition and denoising techniques along with four machine learning classifiers viz. Decision Tree, k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) by using the statistical features of the VMD modes. Among the different methods evaluated, VMD denoised signals when fed to LSTM result in the maximum classification accuracy of 99.14 % with experimental dataset. For the case of CWRU dataset, VMD denoised signals as input to the SVM resulted in maximum classification accuracy of 98.16%.

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.

Construction and application of a bearing fault diagnosis model based on improved GWO algorithm

Construction and application of a bearing fault diagnosis model based on improved GWO algorithm

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

A strong anti-noise and easily deployable bearing fault diagnosis model based on time–frequency dual-channel Transformer

Deep learning is widely used in Bearing Fault Diagnosis (BFD). Nonetheless, practical industrial production often generates a large amount of industrial noise. These noises exhibit randomness and complexity, which puts forward higher requirements for diagnosis algorithms. Certain studies have tackled the issue of anti-interference in high-noise environments (SNR≤0dB) by increasing the complexity of the model. However, due to the excessive number of parameters and computation, such models cannot be deployed on low-end edge devices. Balancing resource consumption and accuracy has become a major challenge in BFD modeling research. To solve the above problems, this paper proposes a new Transformer architecture model called LTFAFormer. The LTFAFormer is capable of achieving high-precision diagnostics on low-end edge devices and shows greater noise resistance. In terms of processing sequence information, Transformer has proven to be superior to other solutions. However, when dealing with longer sensor signal data containing complex noise, the traditional self-attention mechanism not only cannot effectively extract fault features, but also generates more computational complexity than CNN. To address this issue, we propose a novel time–frequency dual-channel parallel attention mechanism. Our approach enhances the feature extraction capability of the model by expanding the attention computation scale and reduces the computational resource consumption of the model by optimizing the model structure. To validate the effectiveness of LTFAFormer, we present two cases to demonstrate that LTFAFormer has higher prediction accuracy while satisfying lightweight. Especially in high-noise environments, LTFAFormer has stronger robustness. In this paper provides a new set of feasible strategies for the practical deployment of BFD models in practical industrial environments.

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.

TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network

Motor bearing fault diagnosis is essential to guarantee production efficiency and avoid catastrophic accidents. Deep learning-based methods have been developed and widely used for fault diagnosis, and these methods have proven to be very effective in accurately diagnosing bearing faults. In this paper, study the application of generative adversarial networks (GANs) in motor bearing fault diagnosis to address the practical issue of insufficient fault data in industrial testing. Focus on the auxiliary classifier generative adversarial network (ACGAN), and the data expansion is carried out for small datasets. This paper present a novel transformer network and auxiliary classifier generative adversarial network (TRA-ACGAN) for motor bearing fault diagnosis, where the TRA-ACGAN combines an ACGAN with a transformer network to avoid the traditional iterative and convolutional structures. The attention mechanism is fully utilized to extract more effective features, and the dual-task coupling problem encountered in classical ACGANs is avoided. Experimental results with the CWRU dataset and the PU dataset in the field of motor bearing fault diagnosis demonstrate the suitability and superiority of the TRA-ACGAN.

AdaClass filter and its application in bearing fault diagnosis

The vibration signals recorded by the sensor reflect the operating state of bearings, and extracting recognizable features effectively from them has become a hot issue in fault diagnosis. Currently, signal processing based filtering methods have emerged as a popular approach for extracting fault-related features. However, conventional filters based on specified assumptions and theoretical models have limited adaptability to multiple types of bearings under different operating conditions, which can significantly impact the diagnostic results. Given this, a data-driven Adaptive Class (AdaClass) filter is proposed to extract the response characteristics of different categories within the latent space. The filter details are obtained by statistically analyzing the mean vectors of samples for each class in the reconstructed feature subspaces. Notably, the latent feature space is mapped by linear operators linear discriminant analysis and class-wise principal component analysis, where the data has a more concise feature representation and a more distinct feature structure. The low-dimensional projection operations enhance the differential information among different categories, and reorganize the internal structure within the same category. Furthermore, a bearing fault diagnosis model is developed based on the AdaClass filter banks, utilizing one-step convolution to improve the efficiency of feature extraction. Experimental results show that the proposed method outperforms the competitors in terms of accuracy, time consumption, and noise resistance, especially for small sample scenarios.

A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation

For the issue of significant noise in the collected bearing fault signals, a new bearing fault diagnosis model based on mutual mapping of signals and images (MMSI) and sparse representation (SR) denoising is proposed. Firstly, the fault signal is divided into several segments with the same number of sampling points, and then arrange these segments in ascending order of rows. Secondly, convert the arranged signals into grayscale image and use dictionary learning for block denoising. Then, the de-noised grayscale image is restored to a signal in line order. Finally, k-nearest neighbor (KNN) is used for fault classification. To verify the performance of the proposed model, experiments are tested on 12 single working conditions and 30 multi working conditions on the Case Western Reserve University dataset and the Paderborn dataset. The experimental results indicate that compared with some existing models, the MMSI–SR–KNN model can not only accurately diagnose bearing faults in artificial damage experiments, but also performs better in real damage faults. This indicates that the model has good generalization ability between different datasets and working conditions.

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Research on transfer learning in rolling bearing fault diagnosis can help overcome challenges such as different data distributions and limited fault samples. However, most existing methods still struggle to address the zero-shot cross-domain problem within the same equipment and the few-shot cross-machine problem. In response to these challenges, this paper introduces a transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment. The model consists of two stages. In the first stage, the original vibration signals undergo continuous wavelet transform to obtain time–frequency diagram. Subsequently, a convolutional neural network extracts features from the diagram. The obtained deep features are decomposed into four types: uncertain features, domain-shared features, domain-specific features and category features. Multiple loss functions are then employed to remove extraneous features beyond the category features. In the second stage, category features are further extracted, and convolutional block attention module is introduced to further reduce the potential interference of unexcluded irrelevant information within the category features with classification results. Simultaneously applying a class-level alignment strategy effectively alleviates inter-domain class distribution discrepancies. Experimental validation was conducted on three distinct datasets, revealing a significant improvement in the classification performance of the proposed method over alternative methods. Furthermore, the model demonstrated robustness and noise resistance.

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance.

Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR).

Rolling bearing fault diagnosis model based on DSCB-NFAM

Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on depth-separable convolutional block (DSCB)-non-local feature awareness module (NFAM) is proposed. Firstly, a DSCB is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a NFAM is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

CACDT: an approach to cross-machine bearing fault diagnosis

Mechanical fault transfer diagnosis utilizes the acquired diagnostic knowledge of machinery to address diagnostic issues in the target machinery. This approach demonstrates promising results in overcoming the limitations of incomplete fault information and scarce labeled data in the era of big data. However, when confronted with cross-machine fault diagnosis, the significant domain discrepancies pose challenges to traditional fault diagnostic methods, leading to lower accuracy and learning efficiency. To overcome these problems, this work introduces a novel cross-machine bearing fault diagnosis model called Cross-Domain Adaptive Clustering and Dynamic Threshold. The model comprises a feature extraction network and a classifier, and it achieves intra-domain and inter-domain adaptation via adversarial optimization. The feature extraction network minimizes the adversarial adaptive clustering loss, while the classifier maximizes it. Moreover, the model calculates dynamical thresholds for each class in the target domain and generates pseudo-labels for unlabeled samples. This approach increases labeled samples for each category during early training, resulting in a more robust clustering core and improving the learning efficiency of the model. Experimental results show that, in cross-machine fault diagnosis, when the number of labels in the target domain is 5 and 10, the average accuracy reaches 82% and 95.6%, respectively, which is better than the comparison method. The model effectively distinguishes minority samples in class imbalance experiments, and the dynamic thresholds enhance learning efficiency for complex datasets compared to fixed thresholds.

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network.

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.

Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model

The working environment of rolling bearings is highly complex and often the vibration signal of the bearing is mixed with noise, which makes fault diagnosis challenging. As such, it is imperative to denoise the vibration signal of rolling bearings, extract effective vibration features, and improve classification accuracy. In this research, we propose a rolling bearing fault diagnosis model based on adaptive modified complementary ensemble empirical mode decomposition (AMCEEMD) and a one-dimensional convolutional neural network (1DCNN). Firstly, the AMCEEMD method is proposed. This algorithm is an improved signal processing technique based on CEEMD, which introduces fuzzy entropy and kurtosis values to remove noise and identify impulse signals. The purpose of AMCEEMD is to obtain standard Intrinsic Mode Functions (IMFs) while removing noise. Secondly, we introduce the energy ratio, fuzzy entropy, and kurtosis as selection indices for IMFs. The selection of IMFs is adapted, and the selected IMF features are inputted into 1DCNN for fault classification. Finally, it was validated by two bearing experiments and compared with other classification methods. The classification accuracy of AMCEEMD-1DCNN method in this study is higher than other methods. The effectiveness of the AMCEEMD-1DCNN fault diagnosis model was verified.

An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults

This study proposes an efficient rolling bearing fault diagnosis model of a hybrid neural network with a lightweight attention mechanism. Firstly, to achieve the low complexity of deep learning (DL) computation, data reduction and denoising are performed by sparse convolutional network (principal component analysis and improved complete ensemble empirical modal decomposition of adaptive noise), then processed data is imported to the hybrid neural network model with convolutional block attention module. The bi-directional long short-term memory and sparse convolutional networks are used in the backbone of the model. A lightweight, generalized attention mechanism is introduced to the last layer of the model for enhancing feature learning, which can further improve the diagnostic accuracy and efficiency. Compared with existing DL fault diagnosis models, In simulating the most realistic cross-conditions and cross-platform conditions, which leads to the random nature of fault generation and makes model diagnosis more complex, the proposed method still maintains less running time and excellent diagnostic accuracy. Finally, the experimental results fully prove that the model has reliable robust and efficient, and it achieves the best balance of diagnostic accuracy and diagnostic efficiency of the hybrid DL model.

A Novel Fault Diagnosis of a Rolling Bearing Method Based on Variational Mode Decomposition and an Artificial Neural Network

In recent years, artificial neural networks have been widely used in the fault diagnosis of rolling bearings. To realize real-time diagnosis with high accuracy of the fault of a rolling bearing, in this paper, a bearing fault diagnosis model was designed based on the combination of VMD and ANN, which ensures a higher fault prediction accuracy with less computational time. This paper works from two aspects, including fault feature extraction and neural network structural parameter optimization to obtain an ANN bearing fault diagnosis model with high performance. The raw vibration signals of 10 fault types were divided into training, verification and testing datasets by the random step increment slip method. The variational mode decomposition method was used to decompose the raw vibration signal into several intrinsic mode functions. A new definition of the energy of each intrinsic mode function based on discrete Fourier transform and information entropy method were used as the input for the artificial neural network. Furthermore, the structural parameters of the artificial neural network were designed to obtain a high-performance neural network. The artificial neural network used in this paper had three hidden layers and 13 neurons in each hidden layer. Compared with several machine and deep learning algorithms, the artificial neural network can better fulfill the classification task of rolling bearing fault types with a mean prediction accuracy of 99.3% and computation time of 2.4 s based on a small training dataset.

A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.