Study Of Analog Circuits Fault Diagnosis On Multi-source Information Fusion

As electronic technology develops rapidly the compose and structure of electronic equipments gets more and more complicated, scale gets larger, the fault diagnosis technology is regarded by more people to improve system's safety and security and becomes one of being researched hotspot aspects. Besides, analog circuits fault more easily in electron systems, so in a way the analog circuits' security decides the security of electronic equipments, therefore it becomes more important to research analog circuits' fault diagnosis.In this thesis, firstly, two fault diagnosis methods based on information fusion are adopted, neural network combines fuzzy method and neural network combines D-S method, the essential idea is that primary dispose of mesurement information is done by neural network and integrated diagnosis of disposed result is done by fusion methods. A specific analog circuit's experiment is done by two methods, results indicate that the later is more predominant than the former in the uncertainty of disposing the analog circuits fault diagnosis. Secondly, D-S is proved and simulation results indicate that improved D-S is more predominant than the basic D-S in disposing the conflictions among evidences and diagnosis result is more reliable. Finally, a method is adopted based on SVM's information fusion, experiments indicate that it is more applicable for SVM to diagnose the analog circuits' faults with very small samples, SVM has many merits and a better application foreground.To improve the accuracy and efficiency of diagnosis, a circuit fault diagnosis system based on information fusion is designed with the MATLAB7.0 and Access2003 software platform.