汇报题目：参加The 2017 IEEE International Conference on Prognostics and Health Management (PHM 2017) 参会报告

汇报时间：2017年6月27日（星期二）19:00

汇报地点：科技园西五楼南205会议室

汇报人：李乃鹏

会议名称：The 2017 IEEE International Conference on Prognostics and Health Management

会议时间：19-21 June 2017

会议地点：Dallas, America

会议简介：The 2017 IEEE International Conference on Prognostics and Health Management (PHM 2017) is bringing together the expertise of relevant technical and management communities to facilitate cross-fertilization in this broad interdisciplinary technical area. PHM 2017 is sponsored by the IEEE Reliability Society, which is part of the institute of Electrical and Electronics Engineers, the world’s largest professional association of computer scientists and engineers. The Reliability Society is concerned with the strategies and the best practices for attaining, assessing, assuring, and sustaining system reliability throughout its life cycle. This conference has been hold in America since 2008 and every year and has had a tremendous response from industry, academia and governmental organizations. Specially, the sponsor, the IEEE Reliability Society, encourages doctoral students from different countries and regions to attend this conference based on their studies which has been confirmed by conference organizing committee.

会议交流工作

Oral presentation: Machine Remaining Useful Life Prediction Considering Unit-to-Unit Variability

报告人：李乃鹏

参加论文信息

Title: Machine Remaining Useful Life Prediction Considering Unit-to-Unit Variability

Author: Naipeng Li, Yaguo Lei*, Ningbo Li, Jing Lin

Abstract: Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model

parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.