Fault diagnosis of mine drainage system based on fuzzy Bayesian network

The mine drainage system is developing towards automation and intelligence. The system's structure and function are becoming more and more complex, and the abnormal function and failure of a single component may cause the failure of the whole system. The existing fault diagnosis methods of the mine drainage system have problems, such as difficult implementation, no consideration of the integrity of the system, and low fault diagnosis efficiency. In order to solve the above problems, a fault diagnosis method of mine drainage system based on fuzzy Bayesian network is proposed. Firstly, the fault tree analysis method is used to decompose the fault causes of the system layer by layer, and find out the root cause of the system fault. Secondly, the events in the fault tree are transformed into the nodes of the Bayesian network. The logic gates are transformed into the directed edges and conditional probabilities of the Bayesian network. The Bayesian network is constructed according to the mapping relationship between the fault tree and the Bayesian network. Thirdly, the fuzzy set theory is introduced into the Bayesian network. The correlation strength between fault and symptom is determined by expert evaluation. After fuzzification by triangular fuzzy number, averaging and defuzzification, the conditional probability of the fuzzy Bayesian network is obtained. Finally, according to the prior probability and conditional probability, the fuzzy Bayesian network is used to judge the probability of each root node fault. The simulation software Genie3.0 is used to establish the fuzzy Bayesian network, and the reasoning analysis and diagnosis test are carried out. The results show that the diagnosis accuracy of the method for each fault symptom is above 80%, and the average accuracy is 82.7%. The method can not only determine the specific position and specific components of the fault source, but also find out the weak nodes of the mine drainage system, eliminate potential faults, and improve the reliability and safety of the system.