Research On Expert System Of Cryogenic Gas Turbine Fault Diagnosis

Due to the complex structure, influenced by many factors, and its long-term working in high temperature and high pressure condition, many components of the gas turbine may fail. Once a device malfunction and stop operation, it will cost a lot of money, energy and time to repair and bring huge economic losses, sometimes even threaten to staff's life security. As gas turbine fault diagnosis can greatly reduce maintenance costs, and also can greatly improve the safety and reliability, so gas turbine fault diagnosis system research not only has great theoretical significance, but also have good practical value.Based on the characteristics of gas turbine failure, we analyzed typical gas turbine failure in the actual conditions and have a detailed study of various failure causes and shown characteristics. According to the characteristics of gas turbine,a gas turbine fault diagnosis knowledge base is established, laying the foundation for fault diagnosis.The knowledge that the traditional Expert System can deal is the apparent one, so its inductive capacity is weak, and also has problems of the knowledge acquisition. In this paper, we built a new system NNES by introducing neural network which is advanced and effective in the diagnostic domain. This practice can improve the intelligent level of the system and improve the system performance. By this fault diagnosis expert system, even if we do not master the professional domain knowledge, we can also get the output conclusion directly from the neural network. Compared with the traditional one, the expert system based on the neural network is better at the data processing.Through the study processing, the fault diagnosis expert system of TORNADO turbine based on combined neural network is constructed. With the every situation in detail, the neural network is trained until the desired accuracy is achieved. The leaned network will deal with the diagnosis data and then the diagnosis news will be putout.The experimental results show that the fault diagnosis expert system improves the shortcoming that the traditional expert system has, and the fault diagnosis efficiency is satisfied.