活塞式氢气压缩机排气温度超限故障诊断方法.

In response to the difficulty in accurately describing the fault diagnosis of exhaust temperature exceeding the limit of a piston type hydrogen compressor using traditional two-state fault diagnosis at each stage, and the lack of application of a fault diagnosis system that could finish self-diagnose for diagnosing the exhaust temperature exceeding the limit, the DW-(5.6-13)/(0.2-0.5)-20 oil-free variable frequency piston type hydrogen compressor was taken as the research object, the importance of relevant components were analyzed, and a fault self-diagnosis system was designed. Firstly, the corresponding fuzzy Takagi-Sugeno(T-S) fault tree analysis model was built for the fault, and the T-S fault tree analysis method based on Bayesian network was used to analyze the importance of the bottom event to determine the weak links and the diagnosis sequence of faulty parts. Then, the fuzzy number was used to represent the fault status of the parts, and it was brought into the T-S fault tree analysis algorithm based on Bayesian-network(BN) to predict the occurrence probability of the primary exhaust temperature overrun fault. Finally, a rule-based fault diagnosis expert system was designed to infer and troubleshoot the fault causes of the instance. The research results indicate that after analyzing existing fault data, the rule-based fault diagnosis expert system’s diagnosis results are consistent with the facts, indicating that the system can accurately diagnose the cause of the fault and achieve self-diagnosis. At the same time, combining with the T-S fault tree analysis method based on BN, the system can also realize the prediction of the probability of top event failure, which provides a feasible scheme for the fault diagnosis of the piston hydrogen compressor. [ABSTRACT FROM AUTHOR]