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

Mohamed Samballa Tambadou,Chaoqun Duan,Deng Chao

doi:10.1109/access.2019.2929007

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.

Highlights

Modern industrial facilities are more vulnerable to wear and friction because of the high integration and complexity of rotating machines
To improve the degradation process modeling and at the same time the remaining useful life (RUL) estimation, this paper presents an online degradation signal based-oil anti-wear property that considers: (a) the wear protection degradation (b) the interaction with the environmental conditions and (c) the jump occurring at different transition period
Our research aims to estimate the remaining life of a lubricated system in real time via online visual ferrograph (OLVF) sensor according to the random operating environment

Summary

INTRODUCTION

Modern industrial facilities are more vulnerable to wear and friction because of the high integration and complexity of rotating machines. We would like to develop how the IPCA curve can be used to represent the oil anti-wear degradation path considering the operating environment and estimate the residual life associated to the lubricated system using three sources of information. To improve the degradation process modeling and at the same time the RUL estimation, this paper presents an online degradation signal based-oil anti-wear property that considers: (a) the wear protection degradation (b) the interaction with the environmental conditions and (c) the jump occurring at different transition period. The novelty of the presented model is the stochastic description of the anti-wear property degradation of the lubrication oil considering the environmental factors and the application of three relevant sources of information to estimate the related RUL of the lubricated system. In what follows we described gradually the Bayesian framework applied to update the RUL

OPERATING ENVIRONMENT UPDATING

RUL ESTIMATION

RUL UNDER RANDOM ENVIRONMENT

PARAMETER ESTIMATION

Findings

CONCLUSION