Bearing Fault Research Articles

Fault diagnosis of power equipment based on variational autoencoder and semi‐supervised learning

SummaryThe issue of fault diagnosis in power equipment is receiving increasing attention from scholars. Due to the important role played by bearings in power equipment, bearing faults have become the main factor causing the shutdown of wind turbines units. Therefore, this paper takes bearing equipment as an example for research. In order to solve the problem of insufficient and unbalanced fault sample data of wind turbines bearings, a fault diagnosis (FD) method based on variational autoencoder and semi‐supervised learning is proposed in this paper. Firstly, based on Label Propagation‐random forests (LP‐RFs) and a small number of labeled fault samples, a semi‐supervised learning algorithm is proposed to label the original data samples. Secondly, a small number of training samples are preprocessed by the variational autoencoder to reduce the imbalance of the fault samples. Then, the RFs‐based method is adopted to train the processed fault samples to obtain a mature FD classifier. Finally, the proposed method is applied to FD for bearings, and the results show that the proposed method can realize bearings fault diagnosis (BFD). And meanwhile, the proposed method can also be applied for fault diagnosis in power transmission and transformation systems.

Statistical Control Charts for Proactive Bearings Fault Diagnosis in Turbines: Advancing Predictive Maintenance in Renewable Energy Systems

Statistical Control Charts for Proactive Bearings Fault Diagnosis in Turbines: Advancing Predictive Maintenance in Renewable Energy Systems

Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis

The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines.

Rolling Element Bearing Fault Diagnosis Based on Multi-objective Optimized Deep Auto-encoder

Abstract Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder (MODAE) is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

Bearing defect detection plays a crucial role in the intelligent production of chemical transmission equipment, where timely identification and handling of defective bearings are essential. However, in practical large-scale industrial production, product surface defects are often complex, diverse, and exhibit significant variations in appearance, posing severe challenges to the discriminative ability and detection efficiency of bearing defect detection algorithms. This paper proposes a real-time bearing surface defect detection algorithm, CAC-YOLOv8, which designs the Channel Attenuation Network (CAN) and Compound Pooling Pyramid Spatial Pyramid Pooling Fast (CPPSPPF) structure. Specifically, the model introduces the Channel Attenuation Network to achieve parallel feature extraction, deep feature processing, and feature fusion under different channel numbers, capturing critical features related to bearing defects and thereby improving the inference speed. Subsequently, based on the concept of overlapped receptive fields, a CPPSPPF structure is constructed, utilizing multiple iterations of max-pooling operations with smaller pooling kernel sizes to prevent information loss while expanding the receptive field, thereby strengthening the capturing ability of features at different scales. The experimental results indicate that the proposed CAC-YOLOv8 bearing surface defect detection algorithm, compared to the YOLOv8 model, achieved a 0.3% improvement in mAP@0.5, reduced model size by 14.4%, and enhanced model inference speed by 33.3%. This enables the CAC-YOLOv8 model to significantly improve the real-time performance of bearing defect detection while maintaining high-precision detection. The performance in practical industrial detection demonstrates that the proposed approach has achieved outstanding results in both speed and accuracy.

Wear parameters identification of hydrodynamic bearing based on operational modal analysis and on-rotor sensing technology

Abrasive wear is a predominant fault in hydrodynamic bearings that can deteriorate bearing performance and damage the rotating machine eventually. The present work proposes a novel wear parameters identification method for hydrodynamic bearings based on operational modal analysis (OMA) and on-rotor sensing (ORS) technology. With this framework, the objective function is defined as the sum of squared errors between the theoretical modal frequencies and the identified modal frequencies. For the experiment verification, the ORS technology is used to collect rotor vibration directly. Additionally, the cepstrum editing procedure (CEP) is applied to enhance modal information. The different wear parameters are successfully identified using combined stochastic subspace identification (SSI) and particle swarm optimization (PSO) algorithms in both simulation and experimental signals.

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.

A Multisensor Approach Integrating Cyclostationary Analysis and Evidence Theory for Explainable Bearing Fault Diagnosis

A Multisensor Approach Integrating Cyclostationary Analysis and Evidence Theory for Explainable Bearing Fault Diagnosis

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection

Detecting faults in bearings is essential for the maintenance and operation of rotating machinery. However, achieving high accuracy and noise immunity is challenging due to the involvement of intricate and noisy signals. To address this issue, this paper introduces a multi-scale separable gated convolutional neural network (GCK-MSSC). In the GCK-MSSC model, the gate convolutional kernel replaces the conventional convolutional kernel. It is designed to dynamically adjust the convolution kernel’s weights based on the input features. Additionally, the one-dimensional global attention mechanism is incorporated, enhancing the model’s global awareness within the MSSC framework. The experimental results on two public bearing datasets confirm the performance of the proposed method. It demonstrates improved performance over current leading-edge methods, especially in terms of accuracy, and proves to be significantly robust against various levels of noise. Specifically, it achieves accuracies of 99.45% and 99.78% on the two datasets. Furthermore, even after the addition of noise with a signal-to-noise ratio of 0, it still maintains an accuracy as high as 85.65% (on the Politecnico di Torino dataset).

Digital twin-assisted AI framework based on domain adaptation for bearing defect diagnosis in the centrifugal pump

Bearing defects in centrifugal pumps represent a prevalent source of equipment failure, often resulting in significant downtime and maintenance expenses. Deep learning algorithms can be used to detect defects. However, in the real world, there is always a scarcity of labeled data. To address this challenge, we propose a framework integrating digital twin technology with domain adaptation for accurate diagnosis of bearing defects. The proposed framework leverages the concept of digital twins to create a virtual representation of the pump bearing, enabling real-time monitoring and simulation of operating conditions. Domain adaptation techniques are then applied to transfer knowledge from synthetic data generated by the digital twin to the actual operating environment, overcoming the domain gap between synthetic and real-world data. The results highlight the potential of digital twin-assisted combined with domain adaptation techniques for enhancing predictive maintenance strategies in industrial applications.

Diagnosis of EV Gearbox Bearing Fault Using Deep Learning-Based Signal Processing

Diagnosis of EV Gearbox Bearing Fault Using Deep Learning-Based Signal Processing

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

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

A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions.

Ensuring the smooth operation of rolling bearings requires a precise fault diagnosis. Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.

Rolling Bearing Fault Diagnosis Based on Multi-source Information Fusion

Rolling Bearing Fault Diagnosis Based on Multi-source Information Fusion

An identification method of compound faults of rolling bearings blending variational mode decomposition and vector bispectrum

To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings.

Research on a Wind Turbine Gearbox Fault Diagnosis Method Using Singular Value Decomposition and Graph Fourier Transform.

Gearboxes operate in challenging environments, which leads to a heightened incidence of failures, and ambient noise further compromises the accuracy of fault diagnosis. To address this issue, we introduce a fault diagnosis method that employs singular value decomposition (SVD) and graph Fourier transform (GFT). Singular values, commonly employed in feature extraction and fault diagnosis, effectively encapsulate various fault states of mechanical equipment. However, prior methods neglect the inter-relationships among singular values, resulting in the loss of subtle fault information concealed within. To precisely and effectively extract subtle fault information from gear vibration signals, this study incorporates graph signal processing (GSP) technology. Following SVD of the original vibration signal, the method constructs a graph signal using singular values as inputs, enabling the capture of topological relationships among these values and the extraction of concealed fault information. Subsequently, the graph signal undergoes a transformation via GFT, facilitating the extraction of fault features from the graph spectral domain. Ultimately, by assessing the Mahalanobis distance between training and testing samples, distinct defect states are discerned and diagnosed. Experimental results on bearing and gear faults demonstrate that the proposed method exhibits enhanced robustness to noise, enabling accurate and effective diagnosis of gearbox faults in environments with substantial noise.

An Integrated Dimension Theory and Modulation Signal Bispectrum Technique for Analyzing Bearing Fault in Industrial Fibrizer

Abstract This study investigates the dynamics of roller bearings by utilizing the dimension theory technique to diagnose the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behaviour of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method (DTMM) is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed under various operational conditions. The noisy signal is subsequently eliminated using the Modulation Signal Bispectrum (MSB). The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure fibrizer test. As a result, this technology offers a significant opportunity to execute more cost-effective maintenance work to eliminate the breakdown of machinery.

An accurate lightweight algorithm for bearings fault diagnosis based on DPW ATTCNN model

Rapid and high-precision fault diagnosis of bearings is an essential safeguard for monitoring the safe operation of machinery. Numerous scholars have proposed bearing fault diagnosis models that achieve excellent identification accuracy. However, owing to the elevated cost, some of fault diagnosis models are too complicated to be deployed in practice. This paper proposes a lightweight model based on Depthwise separable convolution (DPW) and Efficient Channel Attention (ECA) called DPW ATTCNN model. The DPW ATTCNN model first adopts DPW to reduce the parameters of the model. Meanwhile, the ECA attention mechanism is utilized to learn crucial information between neural network channels, which improves the accuracy of bearing fault diagnosis. The DPW ATTCNN model is improved based on the Adaptive Batch Normalization (AdaBN) algorithm, which has excellent domain adaptive capability. DPW ATTCNN model achieves excellent performance using a small number of parameters. The experimental results show that the DPW ATTCNN model can achieve 99.58 % accuracy in bearing fault diagnosis, which is verified on the public dataset Case Western Reserve University (CWRU) dataset. In addition, the anti-interference evaluation experiment shows that the accuracy of DPW ATTCNN model for bearing fault diagnosis is still higher than 95 % even under strong noise interference. In the domain adaptive performance test, the DPW ATTCNN (AdaBN) model achieves an average fault recognition rate of 97.35 %, which further verifies the robustness and effectiveness of the proposed DPW ATTCNN algorithm.

An Iterative Resampling Deep Decoupling Domain Adaptation method for class-imbalance bearing fault diagnosis under variant working conditions

Bearing fault diagnosis suffers from unsatisfied accuracy under variant working conditions, due to class-imbalance and distribution discrepancy of faulty data. Class-imbalance will cause classification bias that makes the diagnostic model tend to misclassify faulty classes, while the distribution discrepancy caused by variant working conditions will further reduce the model’s generalization. Thus, this paper is mainly focusing on solving the two challenges simultaneously, and an Iterative Resampling Deep Decoupling Domain Adaptation (IRDDDA) model is proposed. The model consists of a feature extractor, domain classifier, label classifier and feature resampler modules, and it adopts a decoupling two-stage training framework. In the first stage, the model learns domain invariant features from the source domain and target domain based on original class-imbalance datasets. In the second stage, the feature extractor is partially fixed, and the feature-based resampling method is used to adjust the biased classifier caused by the class-imbalance problem. An iterative training strategy is adopted to simultaneously update the upper-layers of the feature extractor and adjust the classifier till convergence. The method is verified on the CWRU open dataset and the dataset collected from our bearing test rig. The verification results show that our method can achieve a better fault diagnostic performance overall, especially in minority faulty classes.