I.5 References 39 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. Mhenni, F., N. Nguyen, and J.-Y. Choley (2014). “Automatic fault tree generation from SysML system models”. In: 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. IEEE, pp. 715–720. doi: 10.1109/AIM.2014.6878163. Mukherjee, S. and A. Chakraborty (2007). “Automated fault tree generation: bridging reliability with text mining”. In: 2007 Annual Reliability and Maintainability Symposium. IEEE, pp. 83–88. doi: 10.1109/RAMS.2007.328096. Nadim, K., A. Ragab, and M. Ouali (2023). “Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining”. In: Journal of Intelligent Manufacturing 34.1, pp. 57–83. doi: 10.1007/s10845-021-01903-y. NASA (2002). Fault Tree Handbook with Aerospace Applications. Handbook. U.S. National Aeronautics and Space Administration. Nauta, M., D. Bucur, and M. Stoelinga (2018). “LIFT: Learning fault trees from observational data”. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11024 LNCS, pp. 306–322. doi: 10.1007/978-3-319-99154-2_19. Niloofar, P. and S. Lazarova-Molnar (2021). “Data-Driven Modelling Of Repairable Fault Trees From Time Series Data With Missing Information”. In: 2021 Winter Simulation Conference (WSC), pp. 1–12. doi: 10.1109/WSC52266.2021.9715375. – (2023a). “Collaborative data-driven reliability analysis of multi-state fault trees”. In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 237.5, pp. 886–896. doi: 10.1177/1748006X221076290. – (2023b). “Data-driven extraction and analysis of repairable fault trees from time series data”. In: Expert Systems with Applications 215, p. 119345. issn: 0957-4174. doi: 10.1016/j.eswa.2022.119345. – (2023c). “Learning Temporal Truth Tables of Dynamic Fault Trees from Time Series Data on Faults”. In: 2023 7th International Conference on System Reliability and Safety (ICSRS). IEEE, pp. 449–453. doi: 10.1109/icsrs59833.2023.10381460. Roth, M., M. Wolf, and U. Lindemann (2015). “Integrated Matrix-based Fault Tree Generation and Evaluation”. In: Procedia Computer Science 44. 2015 Conference on Systems Engineering Research, pp. 599–608. issn: 1877-0509. doi: 10.1016/j.procs. 2015.03.027. Ruijters, E. and M. Stoelinga (2015). “Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools”. In: Computer Science Review 15-16, pp. 29–62. issn: 1574-0137. doi: https://doi.org/10.1016/j.cosrev.2015.03.001. Verkuil, B., C. E. Budde, and D. Bucur (2022). “Automated fault tree learning from continuous-valued sensor data: a case study on domestic heaters”. In: International Journal of Prognostics and Health Management 13.2. doi: 10.36001/ijphm.2022. v13i2.3160. Waghen, K. and M.-S. Ouali (2019). “Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis”. In: Expert Systems with Applications 136, pp. 376–391. doi: 10.1016/j.eswa.2019.06.042. Waghen, K. and M.-S. Ouali (2021). “Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis”. In: Expert Systems with Applications 178, p. 115035. issn: 0957-4174. doi: 10.1016/j.eswa.2021.115035. – (2022). “A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System”. In: Algorithms 15.6, p. 178. doi: 10.3390/a15060178.
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