A grouping module assessment method for ocean engineering systems: Subsea tree system as a case

Abstract

In the ocean engineering environment, the quality failure data of complex systems are difficult to obtain due to the high experiment cost. In addition, using a single model to analyze risk, reliability, availability, and maintainability is a big challenge. Based on the fault tree, Dynamic Bayes and Markov models, a state assessment method for ocean engineering systems with multiple maintenance modes is proposed in this paper. This method gives full play to the advantages of the three methods, and comprehensively analyzes the risk, reliability, availability and maintainability. This method uses fault tree and Markov model to pre-process fault data, and then inputs the pre-processed fault data into the multi-state degradation model based on dynamic Bayesian theory. Considering the maintenance strategies of no repair, perfect repair, imperfect repair and preventive repair, the model is iterated and adjusted until the model has processed all the event data and the updated model can best reflect the state of the system. The method is verified by taking the subsea tree of the subsea production system as an example. The obtained tree reliability index (mean time without failure) is basically consistent with the failure statistics of offshore and onshore reliability databases, which verifies the accuracy of the proposed method.