مقاله انگلیسی رایگان در مورد مدل مخفی مارکوف با مشاهدات خود همبستگی برای پیش بینی عمر مفید باقی مانده – الزویر 2019

Abstract

In this paper, a hidden Markov model with auto-correlated observations (HMM-AO) is developed to handle the degradation modeling of manufacturing systems. Unlike the standard hidden Markov models (HMMs), the current observation in the HMM-AO model not only depends on the corresponding hidden system state, but also on the previous observations. A novel algorithm using the expectation maximum is presented to estimate the unknown parameters. Furthermore, missing data and noise that accumulate over time are also considered by modifying the proposed model. Then two remaining useful life prediction methods based on the HMM-AO model are developed. Predictive values of more accuracy can be obtained, since the autocorrelation of observations has been considered and the temporal evolution of degradation processes has been described properly. A case study is illustrated to highlight the advantages of HMM-AO and demonstrate the accuracy and efficiency of the prediction methods. Furthermore, an improved maintenance policy is developed based on the results of remaining useful life prediction. Finally, a comparison with a conventional condition-based maintenance policy is provided to prove the performance of this proposed policy.

Introduction

Maintenance policies play important roles in improving the effectiveness of production systems’ operation. In recent years, maintenance strategies combining data-driven reliability models with observed data of condition monitoring has gained more and more attention [1–3]. Diagnosis and prognosis are two important aspects in the framework of maintenance and have been largely researched [4]. The former focuses on the fault detection [5], while the latter devotes to evaluate the current health state of the system as well as to predict its remaining useful life (RUL). An accurate prediction of the RUL could help develop a more economical and effective maintenance policy.