Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study
•A remaining useful life re-prediction method based on Wiener process is proposed.•Current and historical data are used for re-prediction model construction.•DBNs and EM algorithm are combined to solve the uncertainty caused by missing data. With the continuous improvement of the complexity and comp...
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| Veröffentlicht in: | Computers & industrial engineering Jg. 151; S. 106983 |
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01.01.2021
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| Abstract | •A remaining useful life re-prediction method based on Wiener process is proposed.•Current and historical data are used for re-prediction model construction.•DBNs and EM algorithm are combined to solve the uncertainty caused by missing data.
With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by Expectation Maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach. |
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| AbstractList | •A remaining useful life re-prediction method based on Wiener process is proposed.•Current and historical data are used for re-prediction model construction.•DBNs and EM algorithm are combined to solve the uncertainty caused by missing data.
With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by Expectation Maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach. |
| ArticleNumber | 106983 |
| Author | Cai, Baoping Fan, Hongyan Liu, Guijie Liu, Yonghong Ji, Renjie Shao, Xiaoyan Liu, Zengkai |
| Author_xml | – sequence: 1 givenname: Baoping surname: Cai fullname: Cai, Baoping email: caibaoping@upc.edu.cn organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China – sequence: 2 givenname: Hongyan surname: Fan fullname: Fan, Hongyan organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China – sequence: 3 givenname: Xiaoyan surname: Shao fullname: Shao, Xiaoyan organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China – sequence: 4 givenname: Yonghong surname: Liu fullname: Liu, Yonghong organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China – sequence: 5 givenname: Guijie surname: Liu fullname: Liu, Guijie organization: Department of Mechanical and Electrical Engineering, Ocean University of China, Qingdao, Shandong 266100, China – sequence: 6 givenname: Zengkai surname: Liu fullname: Liu, Zengkai organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China – sequence: 7 givenname: Renjie surname: Ji fullname: Ji, Renjie organization: National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao, Shandong 266580, China |
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| Keywords | Wiener process Subsea Christmas tree system Remaining useful life Expectation Maximization algorithm Dynamic Bayesian networks |
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