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A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

Shiyuan Yang (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China) (Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China)
Debiao Meng (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China) (Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China)
Hongtao Wang (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China) (Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China)
Zhipeng Chen (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Bing Xu (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China)

International Journal of Structural Integrity

ISSN: 1757-9864

Article publication date: 23 May 2023

Issue publication date: 26 May 2023

94

Abstract

Purpose

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

Design/methodology/approach

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

Findings

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Originality/value

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

Keywords

Acknowledgements

This research was funded by the National Natural Science Foundation of China (Grant No. 12232004), the Sichuan Science and Technology Program (Grants Nos 2022YFQ0087 and 2022JDJQ0024), the China Postdoctoral Science Foundation (Grant No. 2021M700693) and the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515240010).

Citation

Yang, S., Meng, D., Wang, H., Chen, Z. and Xu, B. (2023), "A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components", International Journal of Structural Integrity, Vol. 14 No. 3, pp. 498-519. https://doi.org/10.1108/IJSI-03-2023-0020

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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