The Research Of Intelligent Fault Diagnosis System Based On Expert System And Neural Network

Fault diagnosis is finding out the reasons that caused the system maladjusted and judging the places and components where the faults happened. The technology of fault diagnosis is a new subject, which has developed for more than forty years. It has very important practical value. In recent years, in order to deal with the more and more complicated systems, with the development of computer and artificial intelligence, the technology of fault diagnosis comes into another phase——intelligent fault diagnosis. It regards knowledge management as the kernel and combines signal processing and modeling processing.Based on the expert system and BP neural network, the application of expert system and BP neural network in the fault diagnosis of hydraulic pressure system and the design of intelligent fault diagnosis system using expert system and BP neural network are introduced in this paper. According to the obtained operational parameter from the hydraulic pressure system and the fault symptom table in knowledge base, the system searches every generating fault symptom. Based on the rule table in expert system it can find all the possible fault causes. Verifying each possible fault cause and comparing the other fault symptom in it, thereby it infers the diagnosis result. The result also obtains from calling neural network knowledge base when it can not search the right match fault cause in the expert system knowledge base.In the expert system, a method which combines the technology of objected-oriented and the method of if-then rule to present knowledge and a construction of knowledge base based on relational database are introduced. And then we use data base technology to built and maintain knowledge base in the part of expert system. In the design of inference machine, display the good man-machine interactive function, using backward and forward ratiocination to infer. In the design of interpretive program, employ preset text and path tagging method to get inference result and explanation of process in detail when users demand. In the neural network, analyzing the hydraulic pressure system to build a basic structure of BP neural network and extract rules of expert system knowledge base as the set of sample and test. After emulations of training and testing in MALTAB, the better valid coefficient set of significance value and threshold value can be adopted as knowledge base of neural network. In the fault diagnosis, proceeds positive-going calculation by using the created neural network structure to infer the final result.After fulfilling all the design of the system, we utilize MCGS configuration software to build a two-dimension dummy model of propellant-servicing system. The emulation of the real operation of hydraulic pressure system is to test the intelligent fault diagnosis system. From the result of experiment, the system is approved to agree the application requirement with better accuracy.This system uses VC++ 6.0 to build the human-machine interface and the whole controlling program, ACCESS 2000 to design the knowledge base and Matlab to complete the design,training and emulation of artificial neural network.The paper distributes a general precept of fault diagnosis for hydraulic pressure system, which is of great significance for designing other similar system. After applying the real time fault diagnosis of fuel-adding system, improves the veracity and speed of faultdiagnosis, saving a large of manpower and material resources and have an effect on safe production.