Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Abstract

Removing noise from health signals is critical in gas path diagnostics of aircraft engines. An efficient noise filtering/denoising method should remove noise without using future data points, preserve important changes, and promote accurate diagnostics without time delay. Machine Learning (ML)-based methods are promising for high fidelity, accuracy, and computational efficiency under the motivation of Intelligent Engines. However, previous ML-based denoising methods are rarely applied in actual engineering practice because they cannot accommodate time series and cannot effectively capture important changes or are limited by the time delay problem. This paper proposes a Convolutional Neural Network Denoising Autoencoder (CNN-DAE) method to build a denoising autoencoder structure. In this structure, a convolutional operation is used to accommodate time series, and causal convolution is introduced to solve the problem of using future data points. The proposed denoising method is evaluated against NASA's Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) software. It has been proved that the proposed method can accommodate time series, remove noise for improved denoising accuracy and preserve the important changes for enhanced diagnostic information. NASA's blind test case results show that Kappa Coefficient of a common diagnostic method using the processed data is 0.731 and is at least 0.046 higher than the other diagnostic methods in the open literature. Processing health signals using the proposed method would significantly promote accurate diagnostics without time delay. The proposed method could support intelligent condition monitoring systems by exploiting historical information for improved denoising and diagnostic performance.

Review–Modern Data Analysis in Gas Sensors

“…103 A CNN based denoising autoencoder technique showed significant efficiency in noise removal. 104 Zhifang et al 41 showed a sparse reconstruction using domain transfer technique to eliminate noise resulting in increased gas sensor performance.…”

Signal denoising for thermal acoustic imaging

“…Deep learning has the potential to learn complex relationships and excel in extracting meaning information [1][2][3][4][5][6][7][8][9][10]. In paper [11], the author proposes the application of Convolutional Neural Network Denoising Auto-Encoders to intelligently filter noise in aircraft engine gas path health signals, enhancing signal quality for improved diagnostic accuracy. U-net is very popular network used in denoising, it includes the encoder and decoder that allows the network to learn important features of the signal.…”