TY - GEN
T1 - Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines
AU - Abbas, Ammar N.
AU - Chasparis, Georgios C.
AU - Kelleher, John D.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deep Reinforcement Learning (DRL)
KW - Input-Output Hidden Markov Model (IOHMM)
KW - Interpretable AI
KW - Predictive maintenance
UR - https://www.scopus.com/pages/publications/85135914203
U2 - 10.1007/978-3-031-12670-3_12
DO - 10.1007/978-3-031-12670-3_12
M3 - Conference contribution
AN - SCOPUS:85135914203
SN - 9783031126697
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 148
BT - Big Data Analytics and Knowledge Discovery - 24th International Conference, DaWaK 2022, Proceedings
A2 - Wrembel, Robert
A2 - Gamper, Johann
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2022
Y2 - 22 August 2022 through 24 August 2022
ER -