ABSTRACT
In many researches the evaluation of reliability and availability is generally focused on simple cases of series and parallel systems or components. But, in real industrial environments, specifically automated systems, the components are configured in a network structure, where the interactions between the components are defined by dependability links making a complex system. Thus, an appropriate method to handle such a problem is the Bayesian network (BN), which is a useful tool to present both qualitative and quantitative representations of the links between the components. The structure of the network reflects the conditional dependencies among the links and components using prior and posterior probabilities. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. In BNs, nodes show random variables that are robot failures in our case. Links imply the conditional dependencies between any two nodes. Each node is associated with a probability function that takes a particular set of values for the node’s parent variables, and gives the probability of the variable represented by the node.
