Abstract
The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.
Highlights
IntroductionThe real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant
Steam turbine generator set is a key device in power plant
A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network
Summary
The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. It can detect the incipient failure as early as possible, determine the location of the fault and identify size and nature of the faults according to the abnormal conditions appearing in the diagnosis process. This paper proposes a real-time intelligent fault diagnosis system by using strata hierarchical artificial neural In this diagnostic system, it can real-time diagnose faults according to vibration feature on conditions of steam turbine generator sets. Because combined the advantage of interpreting the imprecise of fuzzy set with fast training, guaranteed converge and partial generalization of CMAC, this model can meet online fault diagnosis
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