Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions

• Published in: Friction Vol. 12; no. 6; pp. 1272 - 1282
• Main Authors: König, Florian, Wirsing, Florian, Jacobs, Georg, He, Rui, Tian, Zhigang, Zuo, Ming J.
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.06.2024; Springer Nature B.V; SpringerOpen
• Subjects: Adhesive wear; Bayesian analysis; Bayesian inference; Bearings; Contact angle; Corrosion and Coatings; Degradation; Elastohydrodynamic lubrication; Engineering; Experiments; Friction; Load; Mechanical Engineering; Nanotechnology; Neural networks; Physical Chemistry; Physics; Plain bearings; remaining useful life prediction; Research Article; Simulation; Statistical inference; Surfaces and Interfaces; Thin Films; Tribology; Useful life; Wear; wear modeling; wear modeling; remaining useful life prediction; Bayesian inference; plain bearings
• ISSN: 2223-7690; 2223-7704
• DOI: 10.1007/s40544-023-0814-y

This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear.