28 Chapter 1. Introduction IEC 60050-192:2015 - International Electrotechnical Vocabulary - Part 192: Dependability, Definition 192-03-01 (2015). Available at https://www.electropedia.org/iev/ iev.nsf/display?openform&ievref=192-03-01. Accessed: 2024-06-10. International Electrotechnical Commission. International Organization for Standardization (Sept. 2016). Petroleum, petrochemical and natural gas industries — Collection and exchange of reliability and maintenance data for equipment. ISO/TC 67. International Standard confirmed [90.93]. Corrected version published October 2016. International Organization for Standardization. url: https://www.iso.org/standard/64076.html. Javed, K., R. Gouriveau, and N. Zerhouni (2017). “State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at di!erent technology readiness levels”. In: Mechanical Systems and Signal Processing 94, pp. 214–236. issn: 0888-3270. doi: 10.1016/j.ymssp.2017.01.050. Jimenez-Roa, L. A., T. Heskes, T. Tinga, and M. Stoelinga (2023). “Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms”. In: IEEE Transactions on Dependable and Secure Computing 20.4, pp. 3317–3327. doi: 10.1109/TDSC. 2022.3203805. Kabir, S. (2017). “An overview of fault tree analysis and its application in model based dependability analysis”. In: Expert Systems with Applications 77, pp. 114–135. doi: 10.1016/j.eswa.2017.01.058. Kaelbling, L. P., M. L. Littman, and A. W. Moore (1996). “Reinforcement learning: A survey”. In: Journal of artificial intelligence research 4, pp. 237–285. doi: 10.48550/ arXiv.cs/9605103. Kantidakis, G., H. Putter, S. Litière, and M. Fiocco (2023). “Statistical models versus machine learning for competing risks: development and validation of prognostic models”. In: BMC Medical Research Methodology 23.1, p. 51. doi: 10.1186/s12874-023-01866z. Karris, S. (2006). Introduction to Simulink with Engineering Applications. Orchard Publications. isbn: 9780974423975. url: https://books.google.nl/books?id=L2JYZYI_l_wC. Kerkkamp, D., Z. Bukhsh, Y. Zhang, and N. Jansen (2022). “Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks”. English. In: Proceeding of the 14th International Conference on Agents and Artificial Intelligence. Vol. 2, pp. 574–585. doi: 10.5220/0000155600003116. Kim, S., J.-H. Choi, and N. H. Kim (2021). “Challenges and Opportunities of System-Level Prognostics”. In: Sensors 21.22. issn: 1424-8220. doi: 10.3390/s21227655. Lapa, C. M., C. M. Pereira, and A. C. de A. Mol (2000). “Maximization of a nuclear system availability through maintenance scheduling optimization using a genetic algorithm”. In: Nuclear Engineering and Design 196.2, pp. 219–231. issn: 0029-5493. doi: 10.1016/ S0029-5493(99)00295-2. Lee, J., F. Wu, W. Zhao, M. Gha!ari, L. Liao, and D. Siegel (2014). “Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications”. In: Mechanical Systems and Signal Processing 42.1, pp. 314–334. issn: 0888-3270. doi: 10.1016/j.ymssp.2013.06.004. Lee, J., C. Y. Park, S. Baek, S. H. Han, and S. Yun (2021). “Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective”. In: Sustainability 13.13. issn: 2071-1050. doi: 10.3390/su13137213. M.A. Cardoso, M. A. and M. S. Silva (2016). “Sewer asset management planning – implementation of a structured approach in wastewater utilities”. In: Urban Water Journal 13.1, pp. 15–27. doi: 10.1080/1573062X.2015.1076859. Madden, M. G. and P. J. Nolan (1994). “Generation of fault trees from simulated incipient fault case data”. In: WIT Transactions on Information and Communication Technologies 6. doi: 10.2495/AI940611.
RkJQdWJsaXNoZXIy MjY0ODMw