Remaining Useful Life Prediction Considering Joint Dependency of Degradation Rate and Variation on Time-Varying Operating Conditions

Remaining useful life (RUL) prediction under time-varying operating conditions is critical to the prognostics and health management of rotating machinery. In the literature, both the degradation rate and variation of a machinery component are often assumed to be solely dependent on operating conditions. However, this strong assumption is usually violated in many industrial applications. In this article, a systematic method for RUL prediction for a rotating machinery component is developed by considering the joint dependency of degradation rate and variation on time-varying operating conditions. In particular, a system state function and an observation function are utilized to characterize the component's degradation process. A quantitative relationship between the drift and diffusion parameters is established to reflect their joint dependency on the operating conditions. A two-stage hybrid approach that jointly implements maximum likelihood estimation and least squares estimation methods is proposed to facilitate parameter estimation in model development based on offline degradation data, and a Bayesian algorithm based on online condition monitoring data is utilized for RUL prediction in online implementation. A simulation study and a real application to rolling element bearings are provided to illustrate the effectiveness of the proposed method in practice.

Automated summary

This article presents a systematic method for RUL prediction of rotating machinery components, taking into account the joint dependency of degradation rate and variation on time-varying operating conditions. The proposed method utilizes maximum likelihood estimation and least squares estimation methods for parameter estimation, and a Bayesian algorithm for RUL prediction. Simulation studies and real applications demonstrate the effectiveness of the method.