Title
Remaining Useful Life Prediction With Fusing Failure Time Data And Field Degradation Data With Random Effects
Abstract
Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method.
Year
DOI
Venue
2020
10.1109/ACCESS.2019.2948263
IEEE ACCESS
Keywords
DocType
Volume
Degradation, Bayes methods, Estimation, Parameter estimation, Prediction methods, Prognostics and health management, Data models, Remaining useful life prediction, wiener processes, fusing, failure time data, field degradation data, random effects, Bayesian framework
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Shengjin Tang112.04
Xiaodong Xu211.02
Chuanqiang Yu311.02
Xiaoyan Sun400.68
Hongdong Fan500.68
Xiao-Sheng Si662346.17