Abstract
This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.
Highlights
The reliability of a system or an element is defined as: the ability to perform its required functions under specific operating conditions for a specified period of time [1]
By introducing relevant temporal dependencies between representations, a BN is expanded into a dynamic Bayesian network (DBN), which overcomes the shortcomings of a dynamic fault tree (DFT) [21,25]
To obtain the state probabilities for the Markov process in figure 9a, differential equations are established in equation (5.1) according to equation (2.1)
Summary
The reliability of a system or an element is defined as: the ability to perform its required functions under specific operating conditions for a specified period of time [1]. Traditional analysis methods, such as a fault tree analysis (FTA), a binary decision diagram (BDD) and a failure modes and effects analysis (FMEA), are suggested for the purpose of the reliability evaluation. New methods are required to assess the reliability parameters from the perspective of multi-states or multi-stages to decrease the downtime probability and degradation of complex systems [2,3]
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