Lubrication Oil Anti-Wear Property Degradation Modeling and Remaining Useful Life Estimation of the System Under Multiple Changes Operating Environment

Abstract

Anti-wear property (AWP) is one of the critical characteristics that define the lubrication performance of the lubrication oil. The AWP deterioration is characterized by the wear rate variation and is often related to the operating environment. Since a low AWP can lead to system failure, proactive means to predict the remaining useful life (RUL) considering the environmental factors is an important practical relevance. This paper presents a stochastic model to determine the oil AWP deterioration in order to predict the RUL of the related system. The model assumes that the operating environment behaves as a continuous-time Markov chain (CTMC). A Bayesian methodology using three sources of information (online degradation information, observed degradation, and environmental data) is applied to update dynamically the RUL. In order to demonstrate the applicability of the proposed model, a case of study is presented. Furthermore, to show the accuracy and effectiveness of the proposed approach, a comparative study is conducted with a previously developed model, which does not consider the operating environment. INDEX TERMS Anti-wear property, Bayesian updating, lubrication oil, varying operating environment.

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

“…In another attempt, three operational states based on flow in dual filters were proposed by Henneberg et al [8]. Mohamed et al [9] that predicted the remaining useful life of lubricating oil, considering the first passage time approach for degradation trajectory and continuous-time Markov chain for the dynamic operating environment. The study also assumed that the number of operating states is known.…”