An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings
In this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, a linear model...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/2/88 |
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| author | Yongze Jin Xubo Yang Junqi Liu Yanxi Yang Xinhong Hei Anqi Shangguan |
| author_facet | Yongze Jin Xubo Yang Junqi Liu Yanxi Yang Xinhong Hei Anqi Shangguan |
| author_sort | Yongze Jin |
| collection | DOAJ |
| description | In this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, a linear model is built, and a Kalman filter (KF) is implemented to determine the failure start time (FST). In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. Then, a double exponential model is established, and the hybrid filtering is proposed to estimate the future trend of CRRMS, which is combined by a particle filter (PF) and an unscented Kalman filter (UKF). At the same time, dynamic failure threshold technology is adaptively used to determine the failure thresholds of different bearings. Furthermore, the RUL is extrapolated at the moment the prediction index exceeds the failure threshold. Finally, the effectiveness and practicability of the proposed method is verified on the bearing dataset given by the PRONOSTIA platform. |
| format | Article |
| id | doaj-art-d495ebeb0fa84fad8851754db9f92d18 |
| institution | DOAJ |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-d495ebeb0fa84fad8851754db9f92d182025-08-20T03:11:18ZengMDPI AGActuators2076-08252025-02-011428810.3390/act14020088An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling BearingsYongze Jin0Xubo Yang1Junqi Liu2Yanxi Yang3Xinhong Hei4Anqi Shangguan5The School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an 710048, ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an 710048, ChinaIn this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, a linear model is built, and a Kalman filter (KF) is implemented to determine the failure start time (FST). In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. Then, a double exponential model is established, and the hybrid filtering is proposed to estimate the future trend of CRRMS, which is combined by a particle filter (PF) and an unscented Kalman filter (UKF). At the same time, dynamic failure threshold technology is adaptively used to determine the failure thresholds of different bearings. Furthermore, the RUL is extrapolated at the moment the prediction index exceeds the failure threshold. Finally, the effectiveness and practicability of the proposed method is verified on the bearing dataset given by the PRONOSTIA platform.https://www.mdpi.com/2076-0825/14/2/88performance degradationremaining useful life predictionhybrid filteringunscented Kalman filter (UKF)particle filter (PF)CEEMDAN |
| spellingShingle | Yongze Jin Xubo Yang Junqi Liu Yanxi Yang Xinhong Hei Anqi Shangguan An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings Actuators performance degradation remaining useful life prediction hybrid filtering unscented Kalman filter (UKF) particle filter (PF) CEEMDAN |
| title | An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings |
| title_full | An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings |
| title_fullStr | An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings |
| title_full_unstemmed | An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings |
| title_short | An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings |
| title_sort | improved nonlinear health index crrms for the remaining useful life prediction of rolling bearings |
| topic | performance degradation remaining useful life prediction hybrid filtering unscented Kalman filter (UKF) particle filter (PF) CEEMDAN |
| url | https://www.mdpi.com/2076-0825/14/2/88 |
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