A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach

Simple item page
dc.contributor.authorDangut, Maren David
dc.contributor.authorJennions, Ian K.
dc.contributor.authorKing, Steve
dc.contributor.authorSkaf, Zakwan
dc.date.accessioned2022-04-04T12:56:49Z
dc.date.available2022-04-04T12:56:49Z
dc.date.issued2022-03-26
dc.description.abstractThe use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.en_UK
dc.identifier.citationDangut MD, Jennions IK, King S, Skaf Z. (2023) A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Computing and Applications, Volume 35, Issue 4, February 2023, pp. 2991–3009en_UK
dc.identifier.issn0941-0643
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07167-8
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17733
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPredictive maintenanceen_UK
dc.subjectDeep learningen_UK
dc.subjectExtremely rare failureen_UK
dc.subjectAuto-encoderen_UK
dc.subjectGRU networken_UK
dc.subjectAircraften_UK
dc.titleA rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approachen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
rare_failure_detection_model-Aircraft-2022.pdf
Size:
2.09 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)