Fault Classification Research Articles

A Literature Review of Fault Detection and Diagnostic Methods in Three-Phase Voltage-Source Inverters

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.

Low-Defect-Density Monolayer MoS2 Wafer by Oxygen-Assisted Growth-Repair Strategy.

Atomic chalcogen vacancy is the most commonly observed defect category in two dimensional (2D) transition-metal dichalcogenides, which can be detrimental to the intrinsic properties and device performance. Here a low-defect density, high-uniform, wafer-scale single crystal epitaxial technology by in situ oxygen-incorporated "growth-repair" strategy is reported. For the first time, the oxygen-repairing efficiency on MoS2 monolayers at atomic scale is quantitatively evaluated. The sulfur defect density is greatly reduced from (2.71 ± 0.65) × 1013 down to (4.28 ± 0.27) × 1012 cm-2, which is one order of magnitude lower than reported as-grown MoS2. Such prominent defect deduction is owing to the kinetically more favorable configuration of oxygen substitution and an increase in sulfur vacancy formation energy around oxygen-incorporated sites by the first-principle calculations. Furthermore, the sulfur vacancies induced donor defect states is largely eliminated confirmed by quenched defect-related emission. The devices exhibit improved carrier mobility by more than three times up to 65.2 cm2 V-1 s-1 and lower Schottky barrier height reduced by half (less than 20 meV), originating from the suppressed Fermi-level pinning effect from disorder-induced gap state. The work provides aneffective route toward engineering the intrinsic defect density and electronic states through modulating synthesis kinetics of 2D materials.

Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting

AbstractIntelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environment. This article addresses some of the challenges of fault classification in wind energy systems by proposing an integrated approach that combines deep learning features with a resampled supervisory control and data acquisition (SCADA) dataset. The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near‐miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite‐particle swarm optimization (AEPSO). The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. Furthermore, the proposed integrated approach shows significance for fault diagnosis enhancement in wind energy systems and advancing the field towards more efficient and reliable operation.

A new approach towards more accurate modeling of mechanical defects in power transformer windings

In many cases, power transformers can continue their normal operation despite mechanical defects in their windings. However, estimating the lifespan and assessing the risk of using a transformer with mechanical defects depend on detailed studies of insulation aging and the distribution of transient voltages along the winding, and consequently, the likelihood of partial discharge. Risk assessment studies can only be effectively conducted through accurate modeling of transformers across a wide frequency range. In this study, an attempt has been made to investigate the impact of winding mechanical defects on the transformer equivalent circuits parameters using finite element method precisely modeling of transformer windings, without applying approximation or common simplifications. To compare the effect of each mechanical defect on the resistance, inductance, and capacitance matrices arrays, an index called fault classification accuracy has been used. The investigations have shown that the capacitance matrix arrays undergo the most significant changes for all types of modeled defects in this research. To examine the impact of defects on the transformer frequency response, a π coupled model that directly uses output matrices of finite element modeling has been employed. The sensitivity of various components of frequency response has also been investigated for the used model.

Security Versus Accuracy: Trade-Off Data Modeling to Safe Fault Classification Systems.

While the data-driven fault classification systems have achieved great success and been widely deployed, machine-learning-based models have recently been shown to be unsafe and vulnerable to tiny perturbations, i.e., adversarial attack. For the safety-critical industrial scenarios, the adversarial security (i.e., adversarial robustness) of the fault system should be taken into serious consideration. However, security and accuracy are intrinsically conflicting, which is a trade-off issue. In this article, we first study this new trade-off issue in the design of fault classification models and solve it from a brand new view, hyperparameter optimization (HPO). Meanwhile, to reduce the computational expense of HPO, we propose a new multiobjective (MO), multifidelity (MF) Bayesian optimization (BO) algorithm, MMTPE. The proposed algorithm is evaluated on safety-critical industrial datasets with the mainstream machine learning (ML) models. The results show that the following hold: 1) MMTPE is superior to other advanced optimization algorithms in both efficiency and performance and 2) fault classification models with optimized hyperparameters are competitive with advanced adversarially defensive methods. Moreover, insights into the model security are given, including the model intrinsic security properties and the correlations between hyperparameters and security.

Defect Recognition and Classification Using Ultrasonic Scattered Signals Based on Convolutional Neural Network

Abstract Carbon Fiber Reinforced Polymer (CFRP) is widely used in the aerospace, aircraft, and railway industries because of its remarkable advantages, such as its lightweight and high strength. Detecting and identifying defects in CFRP is crucial for ensuring its secure and efficient use. Ultrasonic non-destructive testing is one of the most practical and effective methods for defect detection and identification. The ultrasonic scattered signals of defects, which contain abundant defect feature information, are advantageous for defect recognition and classification. However, the anisotropy and inhomogeneity of CFRP, which lead to a low signal-to-noise ratio and poor image quality, pose challenges in flaw recognition. To address the issue, this paper proposes using Convolutional Neural Networks (CNN) methods for defect classification, which can adequately extract information from ultrasonic scattered signals directly. The scattering signals from various types of defects in CFRP, including the different sizes of holes and folds, were selected as inputs for CNN models. Different network models combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with 1D-CNN were employed to classify and identify defect signals. To generate sufficient input sample signals, the data sets were further enlarged by incorporating noise into the original signals. The experimental results showed that using the 1D-LSTM-CNN and 1D-GRU-CNN network models can reduce computational resources while ensuring the accuracy of defect recognition.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder

The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (LSTM-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method’s functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance.

Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning

The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery (RM). The increasing adoption of deep learning in this field is likely due to the vast amounts of automatically generated monitoring data and its robust ability to learn and detect errors effectively. Deep learning techniques generally demonstrate high efficacy, particularly when limited fault data is available or when employing a two-stage diagnostic approach, involving manual feature extraction followed by fault classification. To address these challenges, we introduce the Expansive Dual-Attention Fusion Transformer (EDAF-Transformer), which is trained through a semi-supervised approach using minimal labeled data. Our method begins with a detailed explanation of the foundational theories behind transformers and semi-supervised learning strategies. This establishes the basis of our approach to intelligent fault diagnosis, particularly under the constraints of raw vibration signals and limited labeled data availability. We then present our novel model, the EDAF-Transformer , which comprises two main components: the Expansive Dual-Attention Enhanced Modules and the 1-D Twin Global Self-Attention Transformer Encoder Module. The first component employs an Expansive Dual-branch Attention Enhanced Module (EDAEM) within a dual-branch attention architecture. This design effectively enlarges the receptive field, capturing both global and local features. The second component, the Twin Sparse Self-Attention Fusion Module (TSSAFM), integrates Global Sparse Self-Attention with Squeeze-and-Excitation (SE) attention. This module is designed to enhance feature encoding capabilities while simultaneously reducing computational demands. Furthermore, we implement an uncertainty self-training semi-supervised strategy focused on balanced unlabeled sample selection. This approach significantly enhances the generalizability and performance of the EDAF-Transformer . We extensively tested our semi-supervised fault diagnosis method on publicly available motor bearing data and a specially curated transmission shaft dataset. The results from these tests demonstrate that our method surpasses other intelligent fault diagnosis methods in effectiveness.

CC-De-YOLO: A Multiscale Object Detection Method for Wafer Surface Defect

Abstract Surface defect detection on wafers is crucial for quality control in semiconductor manufacturing. However, the complexity of defect spatial features, including mixed defect types, large scale differences, and overlapping, results in low detection accuracy. In this paper, we propose a CC-De-YOLO model, which is based on the YOLOv7 backbone network. Firstly, the coordinate attention is inserted into the main feature extraction network. Coordinate attention decomposes channel attention into two one-dimensional feature coding processes, which are aggregated along both horizontal and vertical spatial directions to enhance the network’s sensitivity to orientation and position. Then, the nearest neighbor interpolation in the upsampling part is replaced by the CAR-EVC module, which predicts the upsampling kernel from the previous feature map and integrates semantic information into the feature map. Two residual structures are used to capture long-range semantic dependencies and improve feature representation capability. Finally, an efficient decoupled detection head is used to separate classification and regression tasks for better defect classification. To evaluate our model’s performance, we established a wafer surface defect dataset containing six typical defect categories. The experimental results show that the CCDe-YOLO model achieves 91.0% mAP@0.5 and 46.2% mAP@0.5:0.95, with precision of 89.5% and recall of 83.2%. Compared with the original YOLOv7 model and other object detection models, CC-De-YOLO performs better. Therefore, our proposed method meets the accuracy requirements for wafer surface defect detection and has broad application prospects. The dataset containing surface defect data on wafers is currently publicly available on GitHub (https://github.com/ztao3243/Wafer-Datas.git).

Defect recognition and classification from ultrasonic phased array total focusing method imaging based on domain adaption

Abstract With the rapid development of China’s economy, the demand for oil and gas resources is continuously increasing, leading to the expansion of the scale of oil and gas long-distance pipelines. Consequently, the risk of safety incidents in oil and gas pipelines is on the rise. Welding defects are identified as one of the significant factors contributing to safety incidents in long-distance oil and gas pipelines. In comparison to traditional conventional ultrasonic detection techniques, which exhibit low efficiency and accuracy, ultrasonic phased array detection technology has the capability to accurately detect welding defects in oil and gas long-distance pipelines. This is particularly true with the development of total focusing method imaging technology, enabling quantitative defect analysis and localization in oil and gas long-distance pipelines.However, in the current scenario, analyzing the types of defects in massive data from long-distance pipelines requires human intervention, leading to low detection efficiency and subjective influence on result analysis. Addressing challenges such as low efficiency in human evaluation of defects in ultrasonic phased array detection, low accuracy in traditional image recognition methods, strong subjectivity in manually extracting features, and the absence of a standardized industrial dataset for ultrasonic phased array detection defects, especially in the case of limited data on ultrasonic phased array detection of welding defects, this study focuses on small sample defects in phased array detection images. The study proposes an improved ResNet50 algorithm through domain adaptation in a deep neural network, trained on a large-scale model data, and then applied to ultrasonic phased array detection data. This approach achieves accurate identification and classification of typical volumetric defects (such as pores) and typical area defects (such as cracks) in the phased array detection of welding defects, laying the foundation for intelligent recognition of defects in long-distance oil and gas pipelines.

Classification Method of Internal Defects in Metal Materials Based on Ultrasonic Point Cloud Data

Abstract The commonly used methods for analysing defects in metal materials include metallography and scanning electron microscopy(SEM). However, these methods require grinding and polishing of the material surface, which can only obtain the characteristics of material surface defects, making it difficult to effectively detect and characterize internal defects in metal materials. Therefore, a deep neural network model based on ultrasonic point cloud data was proposed in this paper, called the Global and Local Feature Fusion Network (GLFFN), to classify the internal defects in metal materials. Firstly, an ultrasonic microscope was used to perform X-layer scanning on the metal materials, obtaining ultrasonic images at different depths, from which ultrasonic point cloud data was extracted. Based on this, the GLFFN model enhances the classification accuracy of internal defects in metal materials by deep fusion of global and local features of the point cloud. With casting billets as the detection object, which contain three typical internal defects: inclusions, shrinkage cavities, and cracks. The accuracy of the new model for defect classification reached 88.24%, which is superior to the traditional methods, proving the effectiveness of the new model on the classification of internal defects in metal materials.

Lightweight Network Bearing Intelligent Fault Diagnosis Based on VMD-FK-ShuffleNetV2

With the increasing complexity of mechanical equipment and diversification of deep learning models, vibration signals collected from such equipment are susceptible to noise interference. Moreover, traditional neural network models struggle to be effectively deployed in production environments with limited computational resources, severely impacting the accurate extraction and effective diagnosis of FK fault characteristics. In response to this challenge, this study proposes a fault diagnosis method for rolling bearings, integrating a lightweight ShuffleNetV2 network with variational mode decomposition (VMD) and the fast kurtogram (FK) algorithm. Initially, this paper introduces an enhanced FK method where the VMD algorithm is employed for data denoising, extracting FK post-denoising. These feature maps not only preserve critical signal information but also simplify data complexity. Subsequently, these feature maps are utilized to train and test the ShuffleNetV2 model, facilitating effective fault identification and classification. Ultimately, by conducting experimental comparisons with several mainstream lightweight network models, such as MobileNet and SqueezeNet, as well as traditional convolutional neural network models, this study validates the effectiveness of the proposed method in extracting fault characteristics from vibration signals, demonstrating superior diagnostic accuracy and computational efficiency. This provides a novel technical approach for health monitoring and fault diagnosis of industrial bearings and offers theoretical and experimental support for the deployment of lightweight networks in industrial applications.

A fault diagnosis method for analog circuits based on EEMD-PSO-SVM

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

A Zero-Shot Learning Approach for Blockage Detection and Identification Based on the Stacking Ensemble Model.

A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed a zero-shot pipeline blockage detection and identification method based on stacking ensemble learning. The experimental signals were first decomposed using variational modal decomposition (VMD), and then, the information entropy was calculated for each intrinsic modal function (IMF) component to construct the feature sets. Second, the attribute matrix was established according to the attribute descriptions of the defect categories, and the stacking ensemble attribute learner was used for the attribute learning of defect features. Finally, defect identification was accomplished by comparing the similarity within the attribute matrices. The experimental results show that target defects can be identified even without targeted training samples. The model showed better classification performance on the six sets of experimental data, and the average recognition accuracy of the model for unknown defect categories reached 72.5%.

Detection and Classification of EHV Transmission Line Faults Based on Sign of Reactive Power

Detection and Classification of EHV Transmission Line Faults Based on Sign of Reactive Power

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples’ diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines.

A comprehensive review of BMS fault analysis in pure electric vehicles

Pure electric vehicle technology faces numerous technical challenges during the process of independent research and development, with the safety and endurance of power batteries being particularly critical. The Battery Management System (BMS) is a core factor affecting the performance and safety of electric vehicles. The normal operation of the BMS is essential for ensuring the reliability of electric vehicles. However, in practical applications, the BMS may encounter faults that can lead to a decline in battery performance, a reduction in lifespan, and even trigger safety incidents. Therefore, the development of an efficient and accurate fault analysis and diagnostic system is particularly important. This paper systematically explores the fault issues of electric vehicle BMS, including the composition, working principles, and main functions of the BMS, as well as the classification, level definition, case analysis, and resolution measures of faults. The article also provides a detailed introduction to fault analysis methods, including qualitative and quantitative analyses. Finally, based on recent hot topics and the latest research progress, it is concluded that model-based fault diagnosis will become increasingly important. This paper aims to provide a reference for the development of the field of electric vehicle battery management fault diagnosis technology.

CNN-ELMNet: fault diagnosis of induction motor bearing based on cross-modal vector fusion

Abstract As the primary driving equipment in industrial, accurate fault diagnosis and condition monitoring of induction motor is crucial for ensuring operational safety. This paper focuses on the bearing faults of induction motors, which have a substantial impact on both the mechanical and electromagnetic systems of the motors. However, in diagnostic tasks, we are faced with the challenges of multi-source, multi-modal data, significant influence from environmental noise, and minimal differentiation between fault data. This paper proposed a novel cross-modal vector fusion fault diagnosis and classification model (CNN-ELMNet), which includes a cross-modal vector fusion network (VF) based on D-S evidence theory, feature extraction layer (FE) and classification layer (CL). Specifically, the VF prioritizes the integration of diagnostic results from individual vibration signals or stator current signals within convolutional neural networks with the features of the input implicit vectors as decision-making evidence, followed by weighted vector fusion through D-S evidence theory at the decision level. The FE focuses on retaining the convolutional, pooling, and fully connected layers of the convolutional network and freezing the final fully connected layer, thus preserving training parameters and fully utilizing the network’s powerful FE capabilities. The CL includes an Extreme Learning Machine optimized for random hyperparameters using the snow ablation optimizer (SAO) algorithm, which offers rapid convergence and high classification recognition rates. The CNN-ELMNet model combines a convolutional network with an extreme learning machine optimized by the SAO algorithm, which not only preserves the model’s FE capability but also enhances the convergence speed and classification recognition rate of the model. Experimental results on real datasets demonstrate that the proposed model exhibits strong stability, generalization, and high accuracy in fault diagnosis, achieving accuracy rate of 99.29% and 98.75%. This provides a more feasible solution for the bearing fault diagnosis of induction motors and holds promising prospects for practical applications.