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Risk-Based Safety Improvements in Railway Asset Management

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International Congress and Workshop on Industrial AI and eMaintenance 2023 (IAI 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

This paper describes results from a research and development (R&D) project at Trafikverket (Swedish transport administration). The purpose of the project is to establish a risk-based framework for continuous safety improvement in railway infrastructure asset management. The approach is based on a combination of methodologies and tools described in dependability standards, such as Fault Tree Analysis (FTA), Failure Modes, Effects and Criticality Analysis (FMECA) and Event Tree Analysis (ETA). The empirical material is related to railway track with focus on safety critical rail defects potentially leading to rail breaks and derailment, but also their management within Trafikverket. Besides identifying risks, barriers and improvements, the approach complies with regulations and mandatory standards. Examples of these are Common Safety Method for Risk Evaluation and Assessment (CSM-RA, EU 402/2013) and EN 50126—RAMS (Reliability, Availability, Maintainability and Safety) for railway applications. In addition, the approach complies with regulatory requirements related to enterprise risk management and internal control, i.e., effectiveness, productivity, compliance and reporting. The approach also supports asset management in accordance with the ISO 55000-series.

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References

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Acknowledgements

We acknowledge the support received from Trafikverket by projects ASSET (TRV 2022/29194) and Reality Lab Digital Railway (TRV 2017/67785). We greatly appreciate management commitment from Björn Dellås and Jonas Larsson, and the specialists’ technical knowledge as input to the analyses.

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Correspondence to Peter Söderholm .

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Söderholm, P., Wikberg, L. (2024). Risk-Based Safety Improvements in Railway Asset Management. In: Kumar, U., Karim, R., Galar, D., Kour, R. (eds) International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-39619-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-39619-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39618-2

  • Online ISBN: 978-3-031-39619-9

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