Convolutional neural network denoising autoencoders for intelligent aircraft engine gas path health signal noise filtering

Simple item page
dc.contributor.authorZhao, Junjie
dc.contributor.authorLi, Yiguang
dc.contributor.authorSampath, Suresh
dc.date.accessioned2022-11-14T14:41:55Z
dc.date.available2022-11-14T14:41:55Z
dc.date.issued2022-10-31
dc.description.abstractRemoving 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.en_UK
dc.identifier.citationZhao J, Li Y-G, Sampath S (2023) Convolutional neural network denoising autoencoders for intelligent aircraft engine gas path health signal noise filtering. Journal of Engineering for Gas Turbines and Power, Volume 145, Issue 6, June 2023, Article number 061013, Paper number GTP-22-1234en_UK
dc.identifier.issn0742-4795
dc.identifier.urihttps://doi.org/10.1115/1.4056128
dc.identifier.urihttps://asmedigitalcollection.asme.org/gasturbinespower/article/145/6/061013/1149529/Convolutional-Neural-Network-Denoising-Auto
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18706
dc.language.isoenen_UK
dc.publisherAmerican Society of Mechanical Engineersen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAircraft engine diagnosticsen_UK
dc.subjectTime-series health signalsen_UK
dc.subjectNoise filteringen_UK
dc.subjectConvolutional Neural Network Denoising Autoencodersen_UK
dc.titleConvolutional neural network denoising autoencoders for intelligent aircraft engine gas path health signal noise filteringen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Convolutional_neural_network_denoising_autoencoders-2022.pdf
Size:
2.63 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)