Electrostatic Monitoring of Gas Path Debris for Aero-engines
We present advanced condition monitoring technology based on electrostatic induction for detecting the debris in aero-engines exhaust gas. We also discuss the key technologies related to electrostatic monitoring systems, such as sensing technology, signal processing, feature extraction, and abnormal particle identification. The finite element method and data fitting method are applied to analyze the sensing characteristics of the sensor. We apply empirical mode decomposition and independent component analysis to effectively remove the noise mixed in with the monitoring signal. Certain diagnostic features extracted from the de-noised signal are presented here. A knowledge-acquisition model based on rough sets theory and artificial neural networks is constructed to identify the abnormal particles. The experiment results show the effectiveness of the methods proposed in this paper, and provide some guidelines for future research in this field for the aviation industry.
aero-engine condition monitoring signal processing feature extraction electrostatic sensor knowledge acquisition