Fault diagnosis on power plant with information fusion technology

Abstract

The monitoring of operation states and fault diagnosis system of turbines in power plant are significant to guarantee the units long-term safety and economic operation. The fault diagnosis of turbines is influenced by various factors, the information provided by every sensor need to be used in comprehensive to enhance the accuracy and reliability of fault diagnosis. The paper has presented a fault diagnosis system based on D-S evidence theory and neural network. Firstly, the inputs of neural network were fuzzed through fuzzy membership function, using BP neural network to train and simulate them, but in some cases, the diagnostic results of neural network were unable to determine the fault type accurately, therefore the information fusion was need. The D-S evidence was applied. This method has been proved by the fault diagnosis of turbine equipment in power plant, which can determine the fault type accurately. Particularly, in the case when the fault type can't be determined by using neural network. Therefore, the method was reliable and effective, for fault diagnosis in power plant equipment, it was significant.