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Volume 12
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Article

A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model

by
Pengpeng Zhi
1,2,3,4,
Guoli Yun
5,
Zhonglai Wang
1,2,*,
Peijing Shi
6,
Xinkai Guo
3,
Jiang Wu
4 and
Zhao Ma
7
1
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
2
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China, Dongguan 523808, China
4
Sichuan Province Engineering Technology Research Center of General Aircraft Maintenance, Civil Aviation Flight University of China, Guanghan 618307, China
5
Chengdu Shengming Automobile Technology Co., Ltd., Chengdu 611730, China
6
Institute of Remanufacturing Industry Technology, National Remanufacturing Industry Base, Cangzhou 062450, China
7
AECC Sichuan Gas Turbine Establishment, Chengdu 610500, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(18), 8961; https://doi.org/10.3390/app12188961
Submission received: 11 July 2022 / Revised: 3 September 2022 / Accepted: 3 September 2022 / Published: 6 September 2022
(This article belongs to the Special Issue New Trends in Lifecycle Reliability Engineering)
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Abstract

In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method.
Keywords: multiple response Gaussian process; subset simulation; reliability analysis; active learning function multiple response Gaussian process; subset simulation; reliability analysis; active learning function

Share and Cite

MDPI and ACS Style

Zhi, P.; Yun, G.; Wang, Z.; Shi, P.; Guo, X.; Wu, J.; Ma, Z. A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model. Appl. Sci. 2022, 12, 8961. https://doi.org/10.3390/app12188961

AMA Style

Zhi P, Yun G, Wang Z, Shi P, Guo X, Wu J, Ma Z. A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model. Applied Sciences. 2022; 12(18):8961. https://doi.org/10.3390/app12188961

Chicago/Turabian Style

Zhi, Pengpeng, Guoli Yun, Zhonglai Wang, Peijing Shi, Xinkai Guo, Jiang Wu, and Zhao Ma. 2022. "A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model" Applied Sciences 12, no. 18: 8961. https://doi.org/10.3390/app12188961

APA Style

Zhi, P., Yun, G., Wang, Z., Shi, P., Guo, X., Wu, J., & Ma, Z. (2022). A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model. Applied Sciences, 12(18), 8961. https://doi.org/10.3390/app12188961

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