Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods
Research on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new performance degradation pre...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4676 |
| _version_ | 1797505541902172160 |
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| author | Xiumei Li Huimin Zhao |
| author_facet | Xiumei Li Huimin Zhao |
| author_sort | Xiumei Li |
| collection | DOAJ |
| description | Research on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new performance degradation prediction method based on optimized kernel extreme learning machine (KELM), improved particle swarm optimization (PSO) and Ensemble Empirical Mode Decomposition (EEMD). Firstly, the particle swarm optimization algorithm was improved by adjusting the inertia weight and linear learning factor and introducing a disturbance term, namely WCDPSO. Then, the penalty coefficient and kernel parameters of KELM were optimized by the WCDPSO, and the WCDPSO-KELM model was obtained. Subsequently, the EEMD method was used to extract original features from sample data, and a performance degradation index is selected from the EEMD feature space, which was input into the WCDPSO-KELM model in order to build a bearing performance degradation prediction trend model. Finally, the proposed method was verified by datasets of rolling bearings that were provided by the PRONOSTIA platform. Experimental results confirmed that the proposed method can efficiently predict the performance degradation trend of rolling bearings. |
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| id | doaj.art-7d1e30f1fbd642bf8f3ef3e53c7ee314 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:20:04Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-7d1e30f1fbd642bf8f3ef3e53c7ee3142023-11-23T07:52:20ZengMDPI AGApplied Sciences2076-34172022-05-01129467610.3390/app12094676Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM MethodsXiumei Li0Huimin Zhao1Software Institute, Dalian Jiaotong University, Dalian 116028, ChinaCivil Aviation University of China, Tianjin 300300, ChinaResearch on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new performance degradation prediction method based on optimized kernel extreme learning machine (KELM), improved particle swarm optimization (PSO) and Ensemble Empirical Mode Decomposition (EEMD). Firstly, the particle swarm optimization algorithm was improved by adjusting the inertia weight and linear learning factor and introducing a disturbance term, namely WCDPSO. Then, the penalty coefficient and kernel parameters of KELM were optimized by the WCDPSO, and the WCDPSO-KELM model was obtained. Subsequently, the EEMD method was used to extract original features from sample data, and a performance degradation index is selected from the EEMD feature space, which was input into the WCDPSO-KELM model in order to build a bearing performance degradation prediction trend model. Finally, the proposed method was verified by datasets of rolling bearings that were provided by the PRONOSTIA platform. Experimental results confirmed that the proposed method can efficiently predict the performance degradation trend of rolling bearings.https://www.mdpi.com/2076-3417/12/9/4676WCDPSO-KELMparticle swarm optimization algorithmEEMDbearings |
| spellingShingle | Xiumei Li Huimin Zhao Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods Applied Sciences WCDPSO-KELM particle swarm optimization algorithm EEMD bearings |
| title | Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods |
| title_full | Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods |
| title_fullStr | Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods |
| title_full_unstemmed | Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods |
| title_short | Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods |
| title_sort | performance prediction of rolling bearing using eemd and wcdpso kelm methods |
| topic | WCDPSO-KELM particle swarm optimization algorithm EEMD bearings |
| url | https://www.mdpi.com/2076-3417/12/9/4676 |
| work_keys_str_mv | AT xiumeili performancepredictionofrollingbearingusingeemdandwcdpsokelmmethods AT huiminzhao performancepredictionofrollingbearingusingeemdandwcdpsokelmmethods |