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The normal running of the aero-engine is an important guarantee to the aviation aircraft flying in safe. As a result, the analysis and processing of the aero-engine vibration signal is an important task, which can realize the running state monitoring and fault diagnosis to the aviation aircraft. Due to the complexity of the aero-engine’s structure, the vibration signals of aero-engine from the sensors fixed upon the aeroengine’s brake often consist of several signals in aliasing, and also contain noise and other disturbance signal among them. The traditional vibration signal processing methods aiming at the anti-interference and de-noising have no significant effect. In addition, due to the non-linearity of the mixed signal, signal feature recognition and extraction is difficulties. This paper presented an application of the BP neural network to the aero-engine vibration signal separation, through the simulation of the aero-engine vibration signal induced by the high pressure rotor and low pressure rotor rotational imbalance; we proved the accuracy of the algorithm, which can separate the aero-engine vibration signal effectively. Through comparing with the fault spectrum characteristics of the aero-engine, the method can predict and diagnosis the aero-engine fault, which has a very important practical value.

Aero-engine Vibration Signal Blind Separation Based on BP Neural Network Algorithm