Research Of BP Neural Network Based Analog Circuit Fault Diagnosis Using Non-Gaussianity Analysis

With the developing of modern electronic technology, the electronic equipment's structure becomes more and more complicated, and its integration and density is also increasing continuously. But the corresponding technology of fault diagnosis of circuits, especially that of analog circuits is developing slowly, which restricts the maintainability of equipments. Therefore, it is of great significance to study the fault diagnosis technology of analog circuits, which has become a hotspot in the fault diagnosis field of circuits nowadays.After summarizing the current technologies of fault diagnosis of analog circuits, this thesis adopts BP neural network algorithm. This paper makes a study of the feature extraction algorithm and the optimizition algorithm of BP neural network, which both are critical sections in the fault diagnosis procedure. This thesis proposes a feature extraction algorithm based on the non-gaussianity analysis and an optimized algorithm of GA-BP neural network. The main work is as follows:1 This paper summarizes the current algorithms in the field of feature extraction, fault diagnosis using artificial intelligence and the optimization of BP neural network, the credits and infects of all those technologies above are also illustrated.2 A feature extraction algorithm based on the non-gaussianity analysis is proposed. It can overcome the problem of large dimension and high computing complexity, which exist in the current algorithms of feature extraction. This algorithm selects the centroid, the kurtosis and engentropy of the signal as the features. The last two features reflect the deviations between a gauss signal and signals sampled in the normal mode and various faulty modes of the circuit. According to the outcome of simulations and actual experiments, this algorithm has the advantage of high definition, low feature dimension and computing simplicity. It is also easy to realize and has generality to some extent.3 The normal GA-BP algorithm has the disadvantages of long codes and poor optimization performance. This thesis puts forward an improved GA-BP algorithm. The methods of coding and fitness computing are optimized to have a short coding length. According to the outcome of simulations and actual experiments, this algorithm can achieve a better designing efficiency, a higher ratio of convergence and a better performance of network.4 A developing method of TPS based on fault binary tree and BP neural network is designed to cope with the larger scale circuits in the equipment. The corresponding developing interface for users is also provided. Finally, the algorithms of this thesis are applied to an ATS to develop the TPS of WJ8615P, which is a VHF receiver. The results of experiment achieve the anticipated performance, proving the feasibility and effectiveness of the algorithms proposed in this paper.