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.
Highlights
In this paper we focus on the reliability estimation for phased-mission system (PMS) but with an emphasis on data-driven method as discussed later
With the mission progressing, the reduced remaining useful life (RUL) of the PMS is significant in contrast with the remaining mission time which is estimated from the condition monitoring (CM) information
This paper contrasts sharply with 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 an individual
Summary
Most of real-world systems operate in phased missions where the reliability structure varies over consecutive time periods, known as phases. In practice, the structure of the mission system at hand is too complicated to determine and the complete knowledge is not always available This leads to a great difficulty to apply these approaches for reliability estimation of a practical PMS. All focuses on a population of common type and there is no work directly establishing the link between the reliability and the historical data/real-time condition monitoring (CM) information of individual PMS in service. These approaches only consider the static scenario with an offline nature.
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