Fault Diagnosis Of Analog Circuit Based On Improved Wavelet Analysis And Neural Network

Fault diagnosis technology of analog circuits was first proposed in 1960. Currently, it has made great academic progresses in the world, and gradually formed a perfect system theory, taken one of the most important roles on circuit theory.At the same time, with the rapid development of the electronics industry, the size and structure of electronic equipment gradually become functionated and modularized. But because of the inherent characteristics in analog circuits such as the non-linearity, continuity and tolerance of component parameters which makes the analog circuits fault diagnosis very difficult. The traditional fault diagnosis technology of analog circuits is difficult to achieve expected effects in practical applications. So with the emergence and development of the artificial intelligence technologies such as neural network, wavelet theory and fuzzy theory has become a new orientation in this filed. In order to solve the problem of the fuzzy and uncertainty of the fault diagnosis of analog circuits that cannot be solved by conventional methods, the modern analog circuit fault diagnosis which using the artificial intelligence brought the new ideas.The paper systematically analyzes the several kinds of traditional and modern diagnostic techniques.And makes intensively studies on the application of fault diagnosis based on the theories of neural network,wavelet analysis, wavelet packet analysis and particle swarmalgorithm. Moreover, to speed up the network convergence speed and the diagnostic accuracy and further enhance the performance of the network, the improved particle swarm algorithms are used to optimize the connection structure of neural network. The main researches of this paper is as follows:(1)Explaining the background significance of analog circuit fault diagnosis technology. Summarizing the traditional fault diagnosis methods and modern diagnosis methods based on artificial intelligence technologies.(2)Explaining the theories of neural network, wavelet transform, wavelet packet transform, and illustrate the superiority of method based on those technologies applied in fault diagnosis with elected circuits.(3)As the key step for fault diagnosis of analog circuits,stress on discusses the extraction of fault feature vector. The methods, wavelet extraction and wavelet packet extraction bounds of this two technologies are explored to extract the fault feature vectors. The superiority of applications on those methods are illustrated with elected circuits.(4)Use PSO algorithm to optimize the wavelet neural network and improve its structure. The result show this improved method can greatly improve performance of the wavelet neural network.