A Data-Driven Reliability Estimation Approach for Phased-Mission Systems. (15th April 2014)

• Record Type: Journal Article
• Authors: He, Hua-Feng; Li, Juan; Zhang, Qing-Hua; Sun, Guoxi
• Other Names: Perc Matjaz Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-04-15
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/283740
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example.