The Research On Fault Diagnosis Method Of Analog Circuit Based On Information Fusion

Analog circuit fault diagnosis is a new multi-subject-crossed technique. It has developed a great deal of theory and approaches for fault diagnosis in the past forty years, which keep at a distance with application. With the rapid development of the Electronic Industry, the importance of the analog circuit fault diagnosis is more and more obvious. It has an important signification for working orderly and reliability design of electronic equipment or system, which need some new and effective theories and methods.Information fusion is a subject formed recently, it has been researched and applied in many fields, however, it is still in starting stage in fault diagnosis of analog circuit. This paper introduces the integration technology of information to the fault diagnosis of analog circuit and set up the general frame of fault diagnosis based on the integration technology of information. Then, a specific diagnosis method was proposed and researched deeply.To the incertitude of fault diagnosis and based on evidence theory, a decision-making fusion method is researched and provided in this paper, with a simulate demonstration to analize. Fault diagnosis method was researched deeply on the basis of neural network-information fusion, theory and realize steps of neural fusion fault diagnosis were proposed, then instantiation simulation was given to prove that use this method can greatly improve the recognition rate of fault diagnosis. One fault diagnosis model and corresponding algorithm was constructed based on neural network and evidence theory for taking a step forward diagnosis correct rate, which can cut down the imput dimension of neural network,improve classification ability,decrease the error classify rate of diagnosis system. Then, the feasibility and effectiveness of this method was manifested by specific diagnosis example.To conquer the problem of acquisition high effectiveness in analog circuit fault imformation, wavelet transform was researched in this paper to preprocess the fault signals. Meanwhile, BP neural network diagnosis method based on wavelet transformation was proposed, this method can abstract fault feature effectively and decreases the dimension of imput vector. By this mean, the construct of neural network can be simplified and training time was economized, then the recognize ability of fault type was improved also.