A Novel Denoising Method for Retaining Data Characteristics Brought from Washing Aeroengines

Abstract

Airlines evaluate the energy-saving and emission reduction effect of washing aeroengines by analyzing the exhaust gas temperature margin (EGTM) data of aeroengines so as to formulate a reasonable washing schedule. The noise in EGTM data must be reduced because they interfere with the analysis. EGTM data will show several step changes after cleaning the aeroengine. These step changes increase the difficulty of denoising because they will be smoothed in the denoising. A denoising method for aeroengine data based on a hybrid model is proposed to meet the needs of accurately evaluating the washing effect. Specifically, the aeroengine data is first decomposed into several components by time and frequency. The amplitude of the component containing the most noise is amplified, and Gaussian noise is added to generate noise-amplified data. Second, a Gated Recurrent Unit Autoencoder (GAE) model is proposed to capture engine data features. The GAE is trained to reconstruct the original data from the amplified noise data to develop its noise reduction ability. The experimental results show that, compared with the current popular algorithms, the proposed denoising method can achieve a better denoising effect, retaining the key characteristics of the aeroengine data.