Fault detection and diagnosis of Aero-Starter-Generator based on spectrum analysis and neural network method

This paper discusses the fault detection and diagnosis of Aero-Starter-Generator. Applying the method of Spectrum Analysis to the motor current to get the characteristics of this signal in frequency domain, and then using them as learning samples to train the network for realizing the mapping relationship between the fault and the spectrum characteristic, this method can be used for detection arid diagnosis of the motor faults efficiently. The fault experiments show that the proposed method can detect and diagnose the faults of Aero-Starter-Generator easily, efficiently and in real-time.