| The equipments in power plants trend to be characterized by high parameters, large-capacity and complexity,its safe and economy operating has a great influence on the society. Once faults occur in the power generation equipment, the influence and loss caused by it would be enormous. Therefore, the operating status of equipments needs to be monitored in time to identify the earlier period symptoms of the faults and make an accurate judgment about the fault positions, degrees as well as fault tendency so as to raise the reliability and availability of the unit. However thermal system is the major production part in power plants and fault diagnosis of this part seems to be especially important. This topic is supported by young people promote fund project in the power industry:Application reseach of the state assessment in power plants based on multi-sensors data fusion technology.This paper begins with how to improve fault diagnosis rate and fault-tolerant, in view of the characteristics with multi-information of thermal system faults in power plants, multi-soruces information fusion technique was introduced into fault diagnosis of equipments which has a good theory significance and application values. Based on the thermal system fault diagnosis in power plant, the main work of the dissertation can be presented as follows in this paper:In term of thermal equipment for more measuring points in power plant, there is a strong correlation between datas and symptoms. A diagnostic method combined principal component analysis with neural network was introduced into the fault identification of condensate system in this paper. Firstly, principal component analysis was used to achieve the optimization features of thermal equipment, the rate of contribution gained from principal component was used to comfirm the input space of the neural network, the advangtages and weakness between principal components combined with BP neural networks and RBF neural network to carry on condensate system fault diagnosis were compared and analyzed. Finally, the effectiveness of the method was verified by diagnosis example of condensate system. The given method in this paper can simplify the structure of neural network and improve classification accuracy of the network.In view of thermal system fault with asynchronous and discrete features, a neural Petri net model for fault diagnosis was put forward and established. Information Entropy was used as attribute reduction standards, the smallest of diagnosis rules can be obtained from a large number of fault information so as to establish the optimal Petri net model. Because fault diagnosis Petri net model lacks self-learning function, neural network was introduced into Petri net.The reaserch results were shown by the fault diagnosis example of condensate system in power plant that the combination of diagnosis method based on information entropy, neural network and Petri net can improve the ability of their respective diagnosis.It can increase the expression ability of net using neural Petri net to model for fault diagnosis system. The method provides an effective way for Petri net to the fault diagnosis of thermal system.Because fault processes of thermal system in power plant partly belong to soft fault, from equipment normal to fault symptoms and then to fault disaster is a slower process in fault during the status changes of many equipments are continous.In response to these characteristics, a hybrid model based on hierarchical integration strategy was provided in this paper. The grey theory, characteristic assessment and neural network were organically integrated to complete the fault subspace identification, according to the multi-attributes decision-making model to achieve comprehensive evaluation of the equipment state. The validity and feasibility of the method was verified through actual data simulation of pulverizing system which is advantageous to get accurate identification to early failure, fault degree as well as fault tendency.
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