Study On Fault Diagnosis For Analog Circuit Based On Wavelet Neural Network

As modern electronic technology develops very rapidly, the research on the fault diagnosis theories and methods for analog circuits is becoming very popular and also challenging. However, traditional fault diagnosis theories and methods cannot meet actual requirement due to difficulties inherent in analog circuits fault diagnosis. On the other hand, computation intelligence technologies including neural network (NN) method and something else are great interest to researchers, which might provide potential solution for fault diagnosis. In this dissertation, based on the theories of modern test technology, system identification, neural network, wavelet analysis, and so on, the application of wavelet neural network (WNN) and multi-wavelet neural network (MWNN) in fault diagnosis for analog circuits is studied. In short, the main works of this dissertation are as follows:1. The NN-based fault diagnosis for analog circuits is studied. Firstly, the structure and principle of analog circuits fault diagnosis system based on the NN are analyzed in detail, on which the application of the BP neural network (BPNN) in fault diagnosis is studied. Then, due to the fact that the BPNN usually converges to local minimum, a new way is considered in this dissertation by using the genetic algorithm (GA) to optimize the weights in the hidden layers of the BPNN. The results show that the optimal parameter of the BPNN can be obtained in short time.2. The research on the different WNNs-based fault diagnosis for analog circuits has been done. Because the traditional BPNN-based fault diagnosis has the following disadvantages such as low convergence rate, easy convergence to local minimum, structure design by experience and false diagnosis. In this dissertation, several WNNs-based fault diagnosis methods for analog circuits are proposed.1) Analog circuits fault diagnosis method based on the BP wavelet neural network (BPWNN) is proposed. At first, wavelets with good time-frequency localization property instead of the activation functions of the traditional BPNN, is used to construct the BPWNN. Then, it is applied in fault diagnosis for analog circuits. Finally, experimental results indicate that the diagnosis efficiency of the method using the BPWNN is better than that of the method using the BPNN, and that the convergence rate of the former is faster than that of the latter.2) Analog circuits fault diagnosis method based on the frame wavelet neural network (FWNN) is proposed. Firstly, a FWNN is constructed according to the theory of wavelet frame. Then, it is applied in analog circuits fault diagnosis. Experimental results show that this method is better than the method using the traditional BPNN. And what's more important, wavelet frame theory can be used as theoretical principle in network structure design.3) Analog circuits fault diagnosis method based on multi-resolution wavelet neural network (MRWNN) is proposed. According to the multi-resolution theory, the MRWNN is constructed in this dissertation. Experimental results show that the method using this MRWNN is characterized by many advantages such as fast convergence rate, avoiding of converging to local minimum, and sound theoretical foundation for structure design. What's more, there is no false diagnosis for existent faults and those new faults can be exactly classified.3. Analog circuits fault diagnosis method based on multi-resolution multi-wavelet neural network (MRMWNN) is proposed. Compared with conventional wavelet, multi-wavelet is simultaneously orthogonality, compact support, symmetry and vanishing moment. In this dissertation, the MRMWNN is constructed and applied in fault diagnosis for analog circuits. This new fault diagnosis method not only keeps the same advantages as those of the MRWNN, but also overcomes the disadvantage inherent in the MRWNN, i.e., dimension disaster. However, it is not easy to construct good multi-wavelet at present.In this dissertation, some research works on the application of wavelet neural network in fault diagnosis for analog circuits have been done and some achievements have been made. In the future, further research will continue to be done.