Gaussian Process Regression-based Surrogate Model for Small-sample Cable Fatigue Life Prediction under Stratified Interpolation

doi:10.3901/JME.260463

Abstract

Abstract: Predicting the fatigue life of cable under extreme temperature variations and dynamic bending loads is a critical challenge in automotive engineering. Conventional experimental testing is time-consuming, costly, and inherently destructive, precluding retesting on the same specimen; while numerical simulations often yield only qualitative or threshold-level results and demand substantial computational resources. To address these limitations, this study proposes a Gaussian process regression-based surrogate model (GPRSM) that integrates physical knowledge with data-driven learning for efficient and accurate fatigue life prediction. A stratified interpolation strategy is first designed to augment sparse experimental data, incorporating physically informed constraints on temperature-dependent life degradation to ensure data completeness and physical consistency across the full operational envelope. A small-sample learning framework is then developed by analyzing the physical correlations among multidimensional parameters—such as bending radius and temperature—enabling high-precision predictions under limited-sample conditions. Leveraging the inherent uncertainty quantification capability of GPRSM, the model outputs both the mean predicted fatigue life and corresponding confidence intervals, providing a quantitative foundation for automotive safety assessment and reliability-driven cable design. Demonstrating an overall R² of 0.86, the method delivers reliable predictions across all operating conditions and conservative estimates at high fatigue lives, aligning well with safety-critical engineering needs. This work presents an efficient and robust technical solution for cable fatigue life prediction, offering significant value for engineering applications and potential for broader adoption in reliability analysis of complex electromechanical systems.

Key words: temperature-load coupling constraints, cable fatigue prediction, Gaussian process regression-based surrogate model (GPRSM), stratified interpolation strategy, small-sample learning method

CLC Number:

TH17

WANG Yun, LI Zhengming, LI Wentao, ZHENG Xuewen, SHEN Guotao. Gaussian Process Regression-based Surrogate Model for Small-sample Cable Fatigue Life Prediction under Stratified Interpolation[J]. Journal of Mechanical Engineering, 2026, 62(14): 198-206.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260463

http://www.cjmenet.com.cn/EN/Y2026/V62/I14/198

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