Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data

Abstract

A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuous-time Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

A Multi-State Model for Reliability Analysis of Metal Sheet Manufacturing Process Using Artificial Neural Network Technique

“…While MLV is an extension of Boolean method, in UGF the distribution of performance output of system is obtained on the basis of performance distribution of its elements. Markov and Semi-Markov process modeling analyze the reliability of MSS under assumption that failure and repair times are exponentially distributed (Lisnianski et al, 2012;Li et al, 2018b;Liu et al, 2014;Fang et al, 2016). Researchers have also used a combination of Markov Model with dynamic Bayesian Network for reliability assessment of MSS (Alyson & Aparna, 2007;Li et al, 2018a).…”