Application of Bayesian network in Production Line System Reliability Analysis

Abstract

Considering the multi-state and uncertainty of causality in complex systems, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed by using the advantage of uncertainty reasoning and multi-state expression of BN. The proposed method is able to identify the system weakness through calculating the system reliability on the basis of multi-state probabilities of elements. The model is applied to a production line system to verify its effectiveness. Bayesian Network (BN)A BN is a directed acyclic graph (DAG), in which the nodes represent variables, arcs signify direct causal relationships between the linked nodes, and the conditional probability table(CPT) assigned to the nodes specify how strongly the linked nodes influence each other [7]. A BN is defined by a set of nodes and a set of directed arcs. A probability is associated to each state of the node. This probability is defined, a priori for a root node and computed by inference for the others. The computation is based on the probabilities of the parent's states and the CPT.Assuming that x1, x2, …, xl are the nodes of BN, the joint probability of all nodes in the network as follows: