ICA在航空发动机振动信号盲源分离中的应用

Blind Source Separation for Aero-engines Vibration Signal by Independent Component Analysis

研究了基于独立分量分析(independent component analysis,简称ICA)的发动机振动信号盲源分离技术,旨在将发动机振动信号按照不同的激振源进行分离。首先阐述了基于最大信噪比的盲源分离算法原理,通过对仿真信号进行分离,判断了分离输出信号与仿真信号的一致性,验证了该算法的可行性;然后将该算法与FFT分离法相结合,应用于某型双转子航空发动机高、低压转子实测振动信号盲源分离中,取得了很好的分离效果,表明应用ICA技术建立的基于最大信噪比的盲源分离算法具有迭代次数少、计算复杂度低、效果好及稳定等优点。

This paper investigated blind source separation of aero-engine vibration signals by using the independent component analysis.Firstly,a blind source separation algorithm based on maximum signal to noise ratio was introduced and adopted to separate simulated signals.The output agreed well with the source signal,which verified correctness and feasibility of the algorithm.Secondly,combination of the above algorithm with fast Fourier transform（FFT）separation method was used in the blind source separation of the measured vibration signals.The results show that the method is effective in separating the high-pressure rotor vibration and the low-pressure rotor vibration of the twin-spool aero-engine.It has some advantages,such as less iteration times,simple calculation process and a high stability.