Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis
With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8906 |
| _version_ | 1797463974051053568 |
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| author | Keshun You Guangqi Qiu Yingkui Gu |
| author_facet | Keshun You Guangqi Qiu Yingkui Gu |
| author_sort | Keshun You |
| collection | DOAJ |
| description | With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s–0.735 N·m/s and 0.735 N·m/s–2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality. |
| first_indexed | 2024-03-09T18:00:20Z |
| format | Article |
| id | doaj.art-b06838684c7d479ebcf95a3ae49e01b8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:20Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b06838684c7d479ebcf95a3ae49e01b82023-11-24T09:57:42ZengMDPI AGSensors1424-82202022-11-012222890610.3390/s22228906Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component AnalysisKeshun You0Guangqi Qiu1Yingkui Gu2School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaWith the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s–0.735 N·m/s and 0.735 N·m/s–2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.https://www.mdpi.com/1424-8220/22/22/8906PHMintelligent fault diagnosiscomplex extreme variable loadinghybrid deep neural networkrobustness and generality |
| spellingShingle | Keshun You Guangqi Qiu Yingkui Gu Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis Sensors PHM intelligent fault diagnosis complex extreme variable loading hybrid deep neural network robustness and generality |
| title | Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis |
| title_full | Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis |
| title_fullStr | Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis |
| title_short | Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis |
| title_sort | rolling bearing fault diagnosis using hybrid neural network with principal component analysis |
| topic | PHM intelligent fault diagnosis complex extreme variable loading hybrid deep neural network robustness and generality |
| url | https://www.mdpi.com/1424-8220/22/22/8906 |
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