A knowledge-based prognostics framework for railway track geometry degradation
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DOI: 10.1016/j.ress.2018.07.004
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Cited by:
- ChiachÃo, Juan & Jalón, MarÃa L. & ChiachÃo, Manuel & Kolios, Athanasios, 2020. "A Markov chains prognostics framework for complex degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
- Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
- Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
- Saleh, Ali & Remenyte-Prescott, Rasa & Prescott, Darren & ChiachÃo, Manuel, 2024. "Intelligent and adaptive asset management model for railway sections using the iPN method," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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Keywords
Railway track degradation; Physics-based modelling; Prognostics; Particle filtering;All these keywords.
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