Neural network modeling of aircraft power plant and fault diagnosis method using time frequency analysis

With the development of manufacturing engineer, the aeroengine structure and operating condition have become more complex and the circumstance is generally under mal-condition with high temperature and pressure, so keeping its reliability and safety of airplane is essential. An effective method for aeroengine fault diagnosis using wavelet neural networks is proposed. The wavelet transform can accurately detect and localize the characteristics of transient signal in time-frequency domain. The advantage of wavelet transform is in achieving flexible frequency resolution logarithmic time frequency bands, thus making it able to extract both high-frequency and low-frequency components from the vibration signal. The characteristic information obtained are input nodes of neural network for fault pattern recognition. The mathematics model for aeroengine fault diagnosis is established and the improved optimization technique for neural network training algorithm is used to accomplish the network parameter identification. By means of enough experiment samples to train the neural network, the fault mode can be obtained from the network output result. Furthermore, the robustness of wavelet network for fault diagnosis is discussed. The results obtained from the application of the method on monitored data collected from a facility validate the utility of this approach.