Multi-state System Reliability Analysis for Ship Power Generation System Based on Markov Reward Model

doi:10.12382/bgxb.2022.0558

Abstract

Abstract:

A multi-state cold standby power generation system of naval ship is investigated. Considering the influences of scheduled maintenance interval on the system reliability parameters, the state-transitions matrix, reward matrix, reward function and acceptability function of power generation system are designed to analyze the system reliability based on Markov reward model (MRM). When the reasonable values are assigned to the elements of reward matrix, several performance measures of interest, such as the system interval availability, mean operational time, mean down time, mean number of system failures and system reliability in the scheduled maintenance interval, are obtained. Then the validity and applicability of the model are implemented through a numerical application. The influences of scheduled maintenance interval and mean time to repair of single generator unit on system reliability measures are demonstrated. In brief, this research provides a modeling tool for the reliability design of ship power generation systemreliability and the technical support for reasonable arrangement of ship maintenance structure. The whole modeling process shows that MRM can be used to analyze and calculate the reliability of complex multi-state system conveniently.

Key words: ship power generation system, reliability, multi-state, Markov reward model

CLC Number:

TB114.3

CHEN Tong, HU Bin, DI Peng. Multi-state System Reliability Analysis for Ship Power Generation System Based on Markov Reward Model[J]. Acta Armamentarii, 2023, 44(10): 3177-3186.

Figures/Tables 10

Fig.1 State-transitions diagram of ship power generation system

Fig.2 Relationship between the system online time t and the system interval availability Ai(t)

Fig.3 Influence of mean time to repair of single unit on the system interval availability Ai(t)

Fig.4 Relationship between system online time t and mean operational time MOTi(t)

Fig.5 Relationship between system online time t and mean down time MDTi(t)

Fig.6 Relationship between system online time t and mean number of system failures Ni(t)

Fig.7 Relationship between the system online time t and system reliability Rei(t) (μM=μS=150times per year)

Fig.8 Relationship between system online time t and system reliability Rei(t) (μM=μS=500 times per year)

Fig.9 The influence of mean time to repair of single unit on the system reliability Rei(1)

Fig.10 Influence of mean time to repair of single unit on the system reliability Rei(3)

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