Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance
Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy....
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1218 |
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| author | Qing Dong Hong Pei Changhua Hu Jianfei Zheng Dangbo Du |
| author_facet | Qing Dong Hong Pei Changhua Hu Jianfei Zheng Dangbo Du |
| author_sort | Qing Dong |
| collection | DOAJ |
| description | Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. |
| format | Article |
| id | doaj-art-725c9c8b34fa4a87b32b83965dfcfcdb |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-725c9c8b34fa4a87b32b83965dfcfcdb2025-08-20T02:45:01ZengMDPI AGSensors1424-82202025-02-01254121810.3390/s25041218Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive MaintenanceQing Dong0Hong Pei1Changhua Hu2Jianfei Zheng3Dangbo Du4The Department of Automation, Rocket Force University of Engineering, Xi’an 710025, ChinaThe Department of Automation, Rocket Force University of Engineering, Xi’an 710025, ChinaThe Department of Automation, Rocket Force University of Engineering, Xi’an 710025, ChinaThe Department of Automation, Rocket Force University of Engineering, Xi’an 710025, ChinaThe Department of Automation, Rocket Force University of Engineering, Xi’an 710025, ChinaPredictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data.https://www.mdpi.com/1424-8220/25/4/1218predictive maintenanceremaining useful liferepairable degrading devicesimperfect maintenanceadaptive Wienersensor-based monitoring |
| spellingShingle | Qing Dong Hong Pei Changhua Hu Jianfei Zheng Dangbo Du Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance Sensors predictive maintenance remaining useful life repairable degrading devices imperfect maintenance adaptive Wiener sensor-based monitoring |
| title | Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance |
| title_full | Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance |
| title_fullStr | Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance |
| title_full_unstemmed | Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance |
| title_short | Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance |
| title_sort | remaining useful life prediction method for stochastic degrading devices considering predictive maintenance |
| topic | predictive maintenance remaining useful life repairable degrading devices imperfect maintenance adaptive Wiener sensor-based monitoring |
| url | https://www.mdpi.com/1424-8220/25/4/1218 |
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