In this article, a probabilistic graph of Bayesian network type is proposed for the isolation of system failure causes. This graph, merge various knowledge on monitored system such as dysfunctional analysis, expert judgment and feedback data. Each new event is considered to adjust the probabilities of the network and effectively guide the new search for the cause of failure. The goal is to improve the isolation process for an efficient maintenance and higher system availability. A case study is used to illustrate the benefits of the proposed approach in isolating monitored system failure causes.

This paper presents an alternating test strategy for multi-state Safety Instrumented Systems (SIS) by considering the proof testing parameters according to the observed state in the current test. An approach based on the model of Dynamic Bayesian Network (DBN) is developed to assess the SIS unavailability. The developed model is used for determining the average system performance according to proof tests parameters for a certain service period. Then, the strategy is applied on SIS actuator layer where a structural redundancy is considered. A case study is presented to illustrate the benefits of the proposed testing strategy in improving the SIS performance.