Aero-engine Fault Diagnosis Based on Multilayer Perceptron BP Neural Network

Abstract

The performance of the aero-engine is an important safeguard to flying security. We can diagnose and predict the fault type through obtaining and analyzing the vibration signals based on the fault characters of the aero-engine. But, because of the complexity of the aero-engine’s structure, the vibration signals acquired from multiple groups of the piezoelectric sensors are often composed of several signal aliasing and other noise jamming, etc. Thus, the vibration signals are in the nonlinear or weak nonlinear state, the traditional blind source separation (BSS) algorithms usually adopt linear hypothesis to approximate equivalent to the nonlinear problems, which leading to the separation results not ideal even wrong. This paper applied a kind of multilayer perceptron BP neural network algorithm to realize the aero-engine vibration signal separation with high precision through the simulation and experiment, which proved this algorithm has a certain practical value to the aero-engine fault diagnosis and prediction.