References
Akiba T., Sano S., Yanase T., Ohta T. & Koyama M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Pages 2623–2631. doi:10.1145/3292500.3330701 Bishop C. & Bishop H. (2023). Deep Learning - Foundations and Concepts. Springer Cham.doi:10.1007/978-3-03145468-4 Bowman, C.F., & Bowman, S.N. (2021). Engineering of Power Plant and Industrial Cooling Water Systems. CRC Press. doi: 10.1201/9781003172437 Calvo-Bascones P., Sanz-Bobi M.A. & Welte T.M. (2021). Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant. Computers in Industry, Vol. 125, 103376. doi: 10.1016/j.compind.2020.103376. Chavan, V.D. & Yalagi, P.S. (2023). A Review of Machine Learning Tools and Techniques for Anomaly Detection. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart
Innovation, Systems and Technologies, Vol 361. Springer. Fujimoto S., van Hoof H. & Meger D (2018). Addressing function approximation error in actor-critic methods. Proceedings of the International Conference on Machine Learning. Vol. 80 Proceedings of the 35th International Conference on Machine Learning, PMLR 80: 15871596. Huang J., You J., Liu H. & Song M (2020). Failure mode and effect analysis improvement: A systematic literature review and future research agenda. Reliability Engineering & System Safety.Vol. 199,106885. doi:10.1016/j.ress.2020.106885 Jones D., Snider C., Nassehi A., Yon J. & Hicks B. (2020) Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, Vol. 29, Part A, pp 36-52. doi:10.1016/j.cirpj.2020.02.002. Maior C. B. S., Araújo L.M.M, Lins I.D., Moura M.D.C. & Droguett E.L. (2023), Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning. IEEE Access, Vol. 11, pp. 2513225151, doi: 10.1109/ACCESS.2023.3255417. Nassif A.B., Talib M.A, Nasir Q. & Dakalbab F.M. (2021), Machine Learning for Anomaly Detection: A Systematic Review. IEEE Access, vol. 9, pp. 78658-78700 doi:
10.1109/ACCESS.2021.3083060.
Ochella S., Shafiee M. & Dinmohammadi F. Artificial intelligence in prognostics and health management of engineering systems (2022), Engineering Applications of Artificial Intelligence, Vol. 108, 104552, doi:
10.1016/j.engappai.2021.104552
Pang G., Shen C, Cao L.& Van Den Henge, A (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys. Vol. 54. Issue 2-38 pp 1-38 doi:10.1145/3439950 Sutton R.S & Barto A.G. (2018). Reinforcement Learning. An Introduction. The MIT Press.
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