Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting
Abstract Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
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| Series: | IET Electric Power Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/elp2.12389 |
| Summary: | Abstract Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t‐SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts. |
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| ISSN: | 1751-8660 1751-8679 |