Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks
For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction f...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2354 |
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| author | Xiangang Cao Fuqiang Zhang Jiangbin Zhao Yong Duan Xingyu Guo |
| author_facet | Xiangang Cao Fuqiang Zhang Jiangbin Zhao Yong Duan Xingyu Guo |
| author_sort | Xiangang Cao |
| collection | DOAJ |
| description | For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average. |
| first_indexed | 2024-04-24T18:35:20Z |
| format | Article |
| id | doaj.art-27b363aec1aa4608bf08b00d86dced4a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:20Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-27b363aec1aa4608bf08b00d86dced4a2024-03-27T13:19:24ZengMDPI AGApplied Sciences2076-34172024-03-01146235410.3390/app14062354Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional NetworksXiangang Cao0Fuqiang Zhang1Jiangbin Zhao2Yong Duan3Xingyu Guo4School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaFor the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.https://www.mdpi.com/2076-3417/14/6/2354multi-domain mixed featuresremaining useful life predictiontemporal convolutional networksfeature dimensionality reductionrolling bearing |
| spellingShingle | Xiangang Cao Fuqiang Zhang Jiangbin Zhao Yong Duan Xingyu Guo Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks Applied Sciences multi-domain mixed features remaining useful life prediction temporal convolutional networks feature dimensionality reduction rolling bearing |
| title | Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks |
| title_full | Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks |
| title_fullStr | Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks |
| title_full_unstemmed | Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks |
| title_short | Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks |
| title_sort | remaining useful life prediction of rolling bearing based on multi domain mixed features and temporal convolutional networks |
| topic | multi-domain mixed features remaining useful life prediction temporal convolutional networks feature dimensionality reduction rolling bearing |
| url | https://www.mdpi.com/2076-3417/14/6/2354 |
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