Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines

Authors

Ammar N. Abbas, Technological University Dublin, IrelandFollow
Georgios C. Chasparis, Software Competence Center Hagenberg, Hagenberg, Austria
John Kelleher, Technological University DublinFollow

Document Type

Article

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

https://link.springer.com/chapter/10.1007/978-3-031-12670-3_12

Abbas, A.N., Chasparis, G.C., Kelleher, J.D. (2022). Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2022. Lecture Notes in Computer Science, vol 13428. Springer, Cham.

https://doi.org/10.1007/978-3-031-12670-3_12

Abstract

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes on combining the advantages of input-output hidden Markov models and reinforcement learning. We propose a novel hierarchical modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while at a low level, provides the optimal replacement policy. This approach outperforms baseline deep reinforcement learning (DRL) models and has performance comparable to that of a state-of-the-art reinforcement learning system while being more interpretable.

DOI

https://doi.org/10.1007/978-3-031-12670-3_12

Recommended Citation

Abbas, Ammar N.; Chasparis, Georgios C.; and Kelleher, John, "Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines" (2023). Conference papers. 411.
https://arrow.tudublin.ie/scschcomcon/411

Funder

This publication is the result of the research and activities done along the Collaborative Intelligence for Safety-Critical systems (CISC) project; which has received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement no. 955901. The research reported in this paper has been performed within the frame of SCCH, part of the COMET Program managed by FFG. The work of Kelleher is also partly funded by the ADAPT Centre which is funded under the Science Foundation Ireland (SFI) Research Centres Program (Grant No. 13/RC/2106_P2).

Creative Commons License

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