A Study On Application Of Fault Detection And Diagnosis Method In Power Plant Based On Artificial Neural Network

The engineering application of the process fault detection and diagnosis technology has been attached more and more importance by people during recent years. There are many kinds of fault detection and diagnosis methods, such as estimation method, rule-base reasoning and pattern recognition techniques, which are applied in different areas. Lately the artificial neural networks have been used successfully in system recognition and pattern recognition tasks, and their suitability for fault detection and diagnosis problems has also been demonstrated.Operation with high safety and efficiency is always the focal point in power plant. Some kinds of faults or malfunctions of equipment or system in power plant may cause disasters. So, it is meaningful to establish the fault detection and diagnosis system in power plant. On the other hand, the power plant is a more complex system, so the fault types in power plant is much more. We can not find a general method to detect or diagnose all kinds of faults in the power plant. To put forth an effective diagnosis method, It is necessary to study the diagnosis techniques in different ways according to different kind of fault in power plant. That is to say, we can use different method to detect and diagnose faults in power plant, but the most important thing is to find the new and practical diagnosis method. Two ways of fault detection and diagnosis using artificial neural network for power plant are provided in this paper, which is based on the analysis in the conventional diagnosis techniques and the principle of artificial neural network. One is based on the ability of system identification of the artificial neural network, the other is based on the ability of pattern recognition of the network.The main works and achievements in this paper are as follows:(1) Based on the ability of the artificial neural network in nonlinear dynamic system identification, set up a prediction model using artificial neural network, we can detect the fault of sensor by using the difference signal between the real measurement value and the prediction value. In addition, we can use the prediction value to substitute for the error measurement value temporarily. After we can exclude the fault of the sensor by other methods, the process fault can be detected according to the difference signal.(2) Based on the ability of the artificial neural network in system identification, set up the static model for the equipment in power plant. According to the analysis of the difference signal, we can detect the lowering of the performance of the equipment.(3) It is possible to diagnose the degree of the performance deterioration of the equipment by using the ability of the artificial neural network in pattern recognition. The results have shown that this method is effective. It can provide a new way for the performance monitoring and diagnosing in power plant, and provide a foundation of predictive maintenance in power plant.(4) Using competitive neural network to diagnose the fault in condenser system. This method can be used to diagnose or classify the faults that can be described by fault symptom.