1 1.9. References 31 Continuous-Time Markov chains, pp. 193–284. isbn: 9780691140629. doi: 10.2307/j. ctvcm4gtc.12. Sutton, R. S. and A. G. Barto (2018). Reinforcement learning: An introduction. MIT press, p. 552. isbn: 978-0262039246. Syan, C. S. and G. Ramsoobag (2019). “Maintenance applications of multi-criteria optimization: A review”. In: Reliability Engineering & System Safety 190, p. 106520. issn: 0951-8320. doi: https://doi.org/10.1016/j.ress.2019.106520. Tahan, M., E. Tsoutsanis, M. Muhammad, and Z. Abdul Karim (2017). “Performancebased health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review”. In: Applied Energy 198, pp. 122–144. issn: 0306-2619. doi: 10.1016/j.apenergy.2017.04.048. Tanwar, M., H. Park, and N. Raghavan (2021). “Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework”. In: Applied Sciences 11.14. issn: 2076-3417. doi: 10.3390/ app11146603. Tinga, T. (Jan. 2013). Principles of Loads and Failure Mechanisms. Applications in Maintenance, Reliability and Design. Springer. Chap. 5, pp. 161–186. isbn: 978-1-44714917-0. doi: 10.1007/978-1-4471-4917-0. Tobon-Mejia, D. A., K. Medjaher, N. Zerhouni, and G. Tripot (2012). “A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models”. In: IEEE Transactions on Reliability 61.2, pp. 491–503. doi: 10.1109/TR.2012.2194177. Tscheikner-Gratl, F., N. Caradot, F. Cherqui, J. P. Leitão, M. Ahmadi, J. G. Langeveld, Y. Le Gat, L. Scholten, B. Roghani, J. P. Rodríguez, et al. (2019). “Sewer asset management–state of the art and research needs”. In: Urban Water Journal 16.9, pp. 662–675. doi: 10.1080/1573062X.2020.1713382. Tsui, K. L., N. Chen, Q. Zhou, Y. Hai, and W. Wang (2015). “Prognostics and Health Management: A Review on Data Driven Approaches”. In: Mathematical Problems in Engineering 2015.1, p. 793161. doi: 10.1155/2015/793161. View of Breda (Aug. 2024). Google Earth. Available from: https://earth.google.com/ web/search/breda/@51.55998129,4.76433911,6.42890411a,46248.16933708d,35y, 0h,0t,0r, Accessed on 2024-08-08. Wang, H., M. Liserre, F. Blaabjerg, P. de Place Rimmen, J. B. Jacobsen, T. Kvisgaard, and J. Landkildehus (2014). “Transitioning to Physics-of-Failure as a Reliability Driver in Power Electronics”. In: IEEE Journal of Emerging and Selected Topics in Power Electronics 2.1, pp. 97–114. doi: 10.1109/JESTPE.2013.2290282. Wang, X., S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao (2024). “Deep Reinforcement Learning: A Survey”. In: IEEE Transactions on Neural Networks and Learning Systems 35.4, pp. 5064–5078. doi: 10.1109/TNNLS.2022.3207346. Wirahadikusumah, R. and D. M. Abraham (2003). “Application of dynamic programming and simulation for sewer management”. In: Engineering, Construction and Architectural Management 10.3, pp. 193–208. doi: 10.1108/09699980310478449. Xia, J. and G. Zou (2023). “Operation and maintenance optimization of o!shore wind farms based on digital twin: A review”. In: Ocean Engineering 268, p. 113322. issn: 0029-8018. doi: 10.1016/j.oceaneng.2022.113322. Xia, T., Y. Dong, L. Xiao, S. Du, E. Pan, and L. Xi (2018). “Recent advances in prognostics and health management for advanced manufacturing paradigms”. In: Reliability Engineering & System Safety 178, pp. 255–268. issn: 0951-8320. doi: https: //doi.org/10.1016/j.ress.2018.06.021. Xue, K., J. Yang, M. Yang, and D. Wang (2023). “An Improved Generic Hybrid Prognostic Method for RUL Prediction Based on PF-LSTM Learning”. In: IEEE Transactions on Instrumentation and Measurement 72, pp. 1–21. doi: 10.1109/TIM.2023.3251391.
RkJQdWJsaXNoZXIy MjY0ODMw