A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. (1st December 2016) - Cai, Baoping; Liu, Hanlin; Xie, Min
Abstract: Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results. Highlights: An OOBN-based real-time fault diagnosis methodology is proposed. The method consists of an off-line construction phase and an on-line diagnosis phase. The application of the methodology is demonstrated using a subsea production system.