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

Abstract

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 aero-engine’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.