Two-dimensional extreme distribution for estimating mechanism reliability under large variance

doi:10.1007/s40436-020-00311-4

Advances in Manufacturing ›› 2020, Vol. 8 ›› Issue (3): 369-379.doi: 10.1007/s40436-020-00311-4

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Two-dimensional extreme distribution for estimating mechanism reliability under large variance

Zhi-Hua Wang^1,2, Zhong-Lai Wang^1,2, Shui Yu^1,2

1 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China;
2 Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China

Received:2019-10-20 Revised:2019-12-05 Published:2020-09-10
Contact: Zhong-Lai Wang E-mail:wzhonglai@uestc.edu.cn
Supported by:
This research was partially supported by National Key R&D Program of China (Grant No. 2017YFB1302301) and the Fundamental Research Funds for Central Universities (Grant No. ZYGX2019J043).

Abstract

Abstract: The effective estimation of the operational reliability of mechanism is a significant challenge in engineering practices, especially when the variance of uncertain factors becomes large. Addressing this challenge, a novel mechanism reliability method via a two-dimensional extreme distribution is investigated in the paper. The time-variant reliability problem for the mechanism is first transformed to the time-invariant system reliability problem by constructing the two-dimensional extreme distribution. The joint probability density functions (JPDFs), including random expansion points and extreme motion errors, are then obtained by combining the kernel density estimation (KDE) method and the copula function. Finally, a multidimensional integration is performed to calculate the system time-invariant reliability. Two cases are investigated to demonstrate the effectiveness of the presented method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00311-4

Key words: Time-variant reliability, Great variance, Twodimensional, Extreme distribution, Kernel density estimation (KDE), Multidimensional, Integration

Zhi-Hua Wang, Zhong-Lai Wang, Shui Yu. Two-dimensional extreme distribution for estimating mechanism reliability under large variance[J]. Advances in Manufacturing, 2020, 8(3): 369-379.

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Two-dimensional extreme distribution for estimating mechanism reliability under large variance

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