Wear Life Prediction Method Based on Geometric Brownian Motion

doi:10.3901/JME.260579

Abstract

Abstract: Aiming at the critical technical challenges in equipment reliability engineering, this study conducts innovative research on wear prediction and service life assessment of mechanical components. Given the limitations of the traditional Wiener process model based on normal distribution assumptions (which exhibit significant deviations from empirical data showing log-normal distribution characteristics under typical failure modes such as abrasive wear and adhesive wear), this research proposes a geometric Brownian motion model based on target increments. By incorporating a variable drift coefficient, the model achieves precise simulation of the dynamic evolution of wear under log-normal distribution conditions. A generalized Wiener model is constructed through logarithmic transformation, enabling lifetime prediction via generalized stress-strength interference theory. Large-sample simulations demonstrate that the proposed method significantly outperforms Wiener process models in both theoretical prediction accuracy and engineering applicability. Further validation through small-sample simulations and an engineering case study of dispensing valve needle wear confirms the method's superior performance in lifetime prediction for lognormally distributed wear scenarios, which provides innovative solutions for formulating preventive maintenance strategies for critical equipment.

Key words: geometric Brownian motion, Wiener process, Monte Carlo, life prediction

CLC Number:

TH117

LIU Shuiqing, TANG Zhikai, XU Haoran, HAN Xu. Wear Life Prediction Method Based on Geometric Brownian Motion[J]. Journal of Mechanical Engineering, 2026, 62(15): 497-506.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260579

http://www.cjmenet.com.cn/EN/Y2026/V62/I15/497

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