Methods For Fault Diagnosis Of Analog Circuits Based On Wavelet Neural Network And System Realizing

Most of the electronic equipment failure is caused by analog circuit fault. with the development of electronic industry, requirements on analog circuit fault diagnosis become higher, so research of methods of analog circuit fault diagnosis is significative. For itself with many difficulties existing, traditional methods of fault diagnosis are hard to achieve the desired results. Neural Networks,which have nonlinear mapping and generalizing ability, provide a powerful way to diagnose faults of analog circuits, morever, methods of combining Wavelet Transform and Neural Network can better deal with some critical problems in analog circuit fault diagnosis.Firstly, this paper illustrates the principle of Neural Network fault diagnosis after understanding basic analog circuit fault diagnosis theories, elaborates approaches of designing BP Network for fault diagnosis. Secondly, this paper researches methods of conbining Wavelet Transform with Neural Network for analog circuit fault diagnosis. for the loose method, using Wavelet decomposition to process the impulse response drastically reduce the number of input fed to the Neural Network, for the compactive method, Wavelet function is used as neural network's inspirit function because of it's good performance, and simulation result indicates that the two methods are feasible and effective by diagnosing a Band-pass filter. Finally, virtual instrument technology is introduced in the area of analog circuit fault diagnosis, LabVIEW procedures are designed to accomplish a fault diagnosis system for an example of negative feedback amplifier circuit, the system can diagnose fault and show fault types intuitively.In a word, the Wavelet Neural Network based approches for fault diagnosis of analog circuits are practical and effective, application of virtual instrumentation to the analog circuit fault diagnosis system, have a good engineering application prospect.