The built-in test equipment in a type of aircraft power system is composed with traditional logic hardware, so the abilities on expansion and reliability are limited hardly. According to the characters of built-in test equipment in the next generation, such as computerization and intelligence, based on the fault pattern analysis of the rectifier in aircraft power system, the key points on frequency to each fault pattern were gained by the frequency analysis on output voltage. By using the multi-resolution analysis in the wavelet theory, the basic wavelet function and scale corresponding to key points was confirmed. And then, the wavelet coefficient was converted to a character vector that is the input of the BP neural network which fulfill the diagnosis by defining frequency energy character vector. The results show that this method can distinguish each fault efficiently.
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