Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance. Dangut, M. D., Jennions, I. K., King, S., & Skaf, Z. Mechanical Systems and Signal Processing, 171:108873, May, 2022. ![Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The 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.
@article{dangut_application_2022,
title = {Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance},
volume = {171},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327022000693},
doi = {10.1016/j.ymssp.2022.108873},
abstract = {The 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.},
language = {en},
urldate = {2022-03-03},
journal = {Mechanical Systems and Signal Processing},
author = {Dangut, Maren David and Jennions, Ian K. and King, Steve and Skaf, Zakwan},
month = may,
year = {2022},
keywords = {Aircraft maintenance, Deep reinforcement learning, Extremely rare event, Imbalance classification},
pages = {108873},
}
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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. 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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. 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