汇报题目：参加The 2018 IEEE International Conference on Prognostics and Health Management (PHM 2018) 参会报告

汇报时间：2017年6月20日（星期三）14:30

汇报地点：科技园西五楼南205会议室

汇报人：闫涛

会议名称：The 2018 IEEE International Conference on Prognostics and Health Management

会议时间：11-13 June 2018

会议地点：Seattle, USA

会议简介：The 2018 IEEE International Conference on Prognostics and Health Management (PHM 2018) is bringing together the expertise of relevant technical and management communities to facilitate cross-fertilization in this broad interdisciplinary technical area. PHM 2018 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 USA 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: Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process

报告人：闫涛

参加论文信息

Title: Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process

Author: Tao Yan, Yaguo Lei, Naipeng Li

Abstract: Remaining useful life (RUL) prediction of machinery is a major task in condition-based maintenance, which is able to provide crucial guidance for preventive maintenance. To guarantee the accuracy for the RUL prediction of machinery subjected to two-phase degradation process, the interactive multiple model (IMM) filtering technique has been used because of its capability in estimating the state and the phase dynamically. However, there are two limitations in the IMM based methods. 1) A crucial parameter of the IMM, i.e., the transition probabilities matrix (TPM) of the IMM, is set manually in existing IMM based methods, which often leads to inaccurate state estimation results. 2) The phase estimation is derived as one-step filtering results without considering the overall evolution of the degradation trend, which is unable to describe the phase transition, thus causing inaccurate phase estimation results. To tackle these two limitations, an improved RUL prediction method is proposed in this paper for machinery subjected to two-phase degradation process. In the proposed method, a two-phase degradation model is constructed to describe the degradation process. A nonlinear IMM technique, i.e., the interactive multiple model particle filter (IMMPF) is utilized for the state and the phase estimation, where the TPM is estimated using the numerical-integration TPM estimation (NI-TPME) algorithm instead of being pre-specified manually. The transition point (TP) distribution is adopted to reflect the overall evolution of the degradation trend, and is further used to modify the phase estimation from the IMMPF. Finally, the RUL is predicted by Monte Carlo simulation. The effectiveness of the proposed method is demonstrated by a numerical simulation study.