Two-stage physics-based Wiener process models for online RUL prediction in field vibration data

•We propose the two-stage physics-based Wiener process models.•An online stage division principle is developed to detect the change point.•An online remaining useful life prediction framework is constructed.•A dataset of wheel tread vibration demonstrates the superiority of the proposed method. Due...

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Vydáno v:	Mechanical systems and signal processing Ročník 152; s. 107378
Hlavní autoři:	Yan, Bingxin, Ma, Xiaobing, Huang, Guifa, Zhao, Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin Elsevier Ltd 01.05.2021 Elsevier BV
Témata:	Accuracy Crack propagation Degradation Failure mechanisms Fatigue crack and spall Fatigue failure Fracture mechanics Mathematical models Nonlinear systems Online RUL prediction Parameter estimation Rotating machinery Statistical inference Treads Two-stage physics-based models Vibration Wheel tread vibration Wiener process model Working conditions Online RUL prediction Wiener process model Two-stage physics-based models Wheel tread vibration Fatigue crack and spall
ISSN:	0888-3270, 1096-1216
On-line přístup:	Získat plný text
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Abstract	•We propose the two-stage physics-based Wiener process models.•An online stage division principle is developed to detect the change point.•An online remaining useful life prediction framework is constructed.•A dataset of wheel tread vibration demonstrates the superiority of the proposed method. Due to most failure mechanisms, such as fatigue crack growth and fatigue spall, the degradation process of rotating machinery commonly exhibits two-stage features in engineering practice. Other minor factors are also the key issues affecting the health evolution process, including the component structure, assembly accuracy, and working environment. Ignoring such a mechanism may lead to imprecise in degradation modeling, life prognostic, and ultimately lead to safety risk. Besides, achieving high accuracy of prognostic emphasizes the influence of random effect in the degradation process. The contribution of this study lies in addressing this issue by proposing two-stage physics-based Wiener process models integrating: (a) fatigue crack mechanism and crack growth law, and (b) other minor factors. A general prognostic framework is formulated by jointly employing the online change point detection, parameter estimation, and remaining useful life (RUL) prediction, which has good statistic inference and applicability in two general nonlinear systems, i.e., power-law and exponential-law. A joint implement of offline two-step parameter estimation method and the online Bayesian update method is executed, making full advantage of historical and in-service data, based on which the RUL prediction transcends into an imperative PHM module. A practical case study on the vibration dataset of wheel treads demonstrates the practically implement ability of the proposed method in achieving high accuracy of RUL prediction.
AbstractList	Due to most failure mechanisms, such as fatigue crack growth and fatigue spall, the degradation process of rotating machinery commonly exhibits two-stage features in engineering practice. Other minor factors are also the key issues affecting the health evolution process, including the component structure, assembly accuracy, and working environment. Ignoring such a mechanism may lead to imprecise in degradation modeling, life prognostic, and ultimately lead to safety risk. Besides, achieving high accuracy of prognostic emphasizes the influence of random effect in the degradation process. The contribution of this study lies in addressing this issue by proposing two-stage physics-based Wiener process models integrating: (a) fatigue crack mechanism and crack growth law, and (b) other minor factors. A general prognostic framework is formulated by jointly employing the online change point detection, parameter estimation, and remaining useful life (RUL) prediction, which has good statistic inference and applicability in two general nonlinear systems, i.e., power-law and exponential-law. A joint implement of offline two-step parameter estimation method and the online Bayesian update method is executed, making full advantage of historical and in-service data, based on which the RUL prediction transcends into an imperative PHM module. A practical case study on the vibration dataset of wheel treads demonstrates the practically implement ability of the proposed method in achieving high accuracy of RUL prediction. •We propose the two-stage physics-based Wiener process models.•An online stage division principle is developed to detect the change point.•An online remaining useful life prediction framework is constructed.•A dataset of wheel tread vibration demonstrates the superiority of the proposed method. Due to most failure mechanisms, such as fatigue crack growth and fatigue spall, the degradation process of rotating machinery commonly exhibits two-stage features in engineering practice. Other minor factors are also the key issues affecting the health evolution process, including the component structure, assembly accuracy, and working environment. Ignoring such a mechanism may lead to imprecise in degradation modeling, life prognostic, and ultimately lead to safety risk. Besides, achieving high accuracy of prognostic emphasizes the influence of random effect in the degradation process. The contribution of this study lies in addressing this issue by proposing two-stage physics-based Wiener process models integrating: (a) fatigue crack mechanism and crack growth law, and (b) other minor factors. A general prognostic framework is formulated by jointly employing the online change point detection, parameter estimation, and remaining useful life (RUL) prediction, which has good statistic inference and applicability in two general nonlinear systems, i.e., power-law and exponential-law. A joint implement of offline two-step parameter estimation method and the online Bayesian update method is executed, making full advantage of historical and in-service data, based on which the RUL prediction transcends into an imperative PHM module. A practical case study on the vibration dataset of wheel treads demonstrates the practically implement ability of the proposed method in achieving high accuracy of RUL prediction.
ArticleNumber	107378
Author	Huang, Guifa Yan, Bingxin Zhao, Yu Ma, Xiaobing
Author_xml	– sequence: 1 givenname: Bingxin surname: Yan fullname: Yan, Bingxin organization: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China – sequence: 2 givenname: Xiaobing surname: Ma fullname: Ma, Xiaobing email: maxiaobing@buaa.edu.cn organization: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China – sequence: 3 givenname: Guifa surname: Huang fullname: Huang, Guifa organization: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China – sequence: 4 givenname: Yu surname: Zhao fullname: Zhao, Yu organization: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
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Keywords	Online RUL prediction Wiener process model Two-stage physics-based models Wheel tread vibration Fatigue crack and spall
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Snippet	•We propose the two-stage physics-based Wiener process models.•An online stage division principle is developed to detect the change point.•An online remaining... Due to most failure mechanisms, such as fatigue crack growth and fatigue spall, the degradation process of rotating machinery commonly exhibits two-stage...
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SubjectTerms	Accuracy Crack propagation Degradation Failure mechanisms Fatigue crack and spall Fatigue failure Fracture mechanics Mathematical models Nonlinear systems Online RUL prediction Parameter estimation Rotating machinery Statistical inference Treads Two-stage physics-based models Vibration Wheel tread vibration Wiener process model Working conditions
Title	Two-stage physics-based Wiener process models for online RUL prediction in field vibration data
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