Remaining useful life prediction for a nonlinear multi-degradation system with public noise

doi:10.21629/JSEE.2018.02.22

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (2): 429-435.doi: 10.21629/JSEE.2018.02.22

• Reliability • Previous Articles Next Articles

Remaining useful life prediction for a nonlinear multi-degradation system with public noise

Hanwen ZHANG¹(), Maoyin CHEN¹(), Donghua ZHOU^1,^2,*()

¹ Department of Automation, Tsinghua University, Beijing 100084, China
² College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Received:2017-01-18 Online:2018-04-26 Published:2018-04-27
Contact: Donghua ZHOU E-mail:zhanghw14@mails.tsinghua.edu.cn;mychen@mail.tsinghua.edu.cn;zdh@mail.tsinghua.edu.cn
About author:ZHANG Hanwen was born in 1989. She is a Ph.D. candidate with the Department of Automation, Tsinghua University. Her research interests are reliability estimation and lifetime prediction. E-mail: zhanghw14@mails.tsinghua.edu.cn|CHEN Maoyin was born in 1975. He is an associated professor with the Department of Automation, Tsinghua University. His research interests are reliability analysis, fault prognosis, and predictive maintenance. E-mail: mychen@mail.tsinghua.edu.cn|ZHOU Donghua was born in 1963. He is the vice president of Shandong University of Science and Technology. His research interests are system identification, fault diagnosis, fault-tolerant control, reliability prediction, and predictive maintenance. E-mail: zdh@mail.tsinghua.edu.cn
Supported by:
the National Natural Science Foundation of China(61290324);the National Natural Science Foundation of China(61473164);the National Natural Science Foundation of China(61490701);the Research Fund for the Taishan Scholar Project of Shandong Province of China(LZB2015-162);This work was supported by the National Natural Science Foundation of China (61290324; 61473164; 61490701) and the Research Fund for the Taishan Scholar Project of Shandong Province of China (LZB2015-162)

Abstract

Abstract:

To predict the remaining useful life (RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization (EM) algorithm. Next, with known thresholds, the RUL of each degradation can be predicted by using the first hitting time (FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.

Key words: remaining useful life (RUL), multi-degradation system, public noise, nonlinear degradation process

Hanwen ZHANG, Maoyin CHEN, Donghua ZHOU. Remaining useful life prediction for a nonlinear multi-degradation system with public noise[J]. Journal of Systems Engineering and Electronics, 2018, 29(2): 429-435.

Figures/Tables 5

Fig 1

Table 1

Estimations of main parameters"

Degradation	${a^{(i)}}$	${\beta ^{(i)}}$	$\theta $
Degradation 1	2.103 9×10?⁹	2.980 1	$\left[ \begin{array}{l}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 1{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 0.956{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 0.938\\0.956{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 1{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 0.979\\0.938{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 0.979{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} 1\end{array} \right]$
Degradation 2	8.369 4×10?³⁰	8.787 59
Degradation 3	4.986 0×10?¹⁹	5.795 0

Table 1

Fig 2

Table 2

Fig 3

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Degradation	Our method	Traditional method
Degradation 1	31 265	37 853
Degradation 2	20 035	22 499
Degradation 3	6 459	8 28722 499
The whole system	10 829	19 663

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Remaining useful life prediction for a nonlinear multi-degradation system with public noise

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