Of Ss8 Electric Locomotive Converter Fault Intelligent Diagnosis System

In this dissertation significances of fault diagnosis are clarified in the beginning. Then the state of studying up till now is summarized. The weakness for Electric Locomotives Main Converter by using principles of fault diagnosis is analyzed. New intelligent fault diagnosis methods employing wavelet transform and neural network for Converter are presented.The structure and principle of the SS8 Converter is introduced in this dissertation. Based on analyse of the Converter's working mechanism, a new method of making the output voltage of the Conveter as fault character parameters is presented. We have got different waveforms of output voltage while there are fault components in the Converter .by making simulink of it by Simulink toolbox. The feature extraction of the waveform is used through wavelet transform. Charater vectors are constructed by obtaining the power spectrum of decomposed coefficients .In this paper, a new efficient intelligent fault detection method based on the wavelet transform and the artificial neural networks was presented, the theoretical background of wavelet transform was given, and a much better BP algorithm based on the traditional BP algorithm was introduced. Accordingly,a new wavelet neural networks fault diagnosis systems was developed.The proposed intelligent fault diagnosis system was simulated .The results showed that the developed system was effective.By using this method,performances of fault diagnosis are improved. The experts can spend less time on analyzing frequency data.The design method of training sets for neural network is discussed.A well designed training sets is benefit to training and improving generalization capability of neural network.The design for the Converter of an on-line detection and intelligent fault diagnosis system was introduced briefly at last.It includes hardware structures and software schemes, block diagrams and procedures as well.