Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance

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dc.contributor.authorDangut, Maren David
dc.contributor.authorJennions, Ian K.
dc.contributor.authorKing, Steve
dc.contributor.authorSkaf, Zakwan
dc.date.accessioned2022-02-15T16:20:48Z
dc.date.available2022-02-15T16:20:48Z
dc.date.issued2022-02-08
dc.description.abstractThe use of aircraft operational logs to predict potential failure that may lead to disruption poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity assets, failures are extremely rare, and hence the distribution of relevant log data containing prior indicators will be highly skewed to the normal (healthy) case. This will present a significant challenge in using data-driven techniques because the model will be biased to the heavily weighted no-fault outcomes. This paper presents a novel approach for predicting unscheduled aircraft maintenance action based on deep reinforcement learning techniques using aircraft central maintenance system logs. The algorithm transforms the rare failure prediction problem into a sequential decision-making process that is optimised using a reward system that penalises proposed predictions that result in a false diagnosis and preferentially favours predictions that result in the right diagnosis. The validation data is directly associated with the physical health aspects of the aircraft components. The influence of extremely rare failure prediction on the proposed method is analysed. The effectiveness of the new approach is verified by comparison with previous studies, cost-sensitive and oversampling methods. Performance was evaluated based on G-mean and false-positives rates. The proposed approach shows the superior performance of 20.3% improvement in G-mean and 97% reduction in false-positive rate.en_UK
dc.identifier.citationDangut MD, Jennions IK, King S, Skaf Z. (2022) Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance. Mechanical Systems and Signal Processing, Volume 171, May 2022, Article number 108873en_UK
dc.identifier.issn0888-3270
dc.identifier.urihttps://doi.org/10.1016/j.ymssp.2022.108873
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17569
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectExtremely rare eventen_UK
dc.subjectDeep reinforcement learningen_UK
dc.subjectImbalance classificationen_UK
dc.subjectAircraft maintenanceen_UK
dc.titleApplication of deep reinforcement learning for extremely rare failure prediction in aircraft maintenanceen_UK
dc.typeArticleen_UK

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