A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector

Featured Application The reliability of many engineering systems (such as offshore and subsea equipment, aerospace equipment, energy production equipment, etc.) is very important, but due to a lack of sufficient failure data, their reliability is difficult to accurately evaluate. If the data is obtained through experiments, it will consume a huge amount of money, manpower and time, and there is no suitable experiment to perform an accurate simulation. The method in this paper completely solves this problem. The method proposed in this paper can not only obtain accurate reliability and availability, but also evaluate the impact of uncertain events on system reliability during equipment operation. In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.