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<p>The purpose of this paper is to describe a risk-based scenario analysis, by use of an Event Tree Analysis approach, which will support testability considerations and test level integration in the design or dependability improvement phases of technical systems. The proposed scenario analysis includes fault recognition and fault localization efforts and their associated hazards of false alarms, unrecognized faults, and un-localized faults. The combination of these single hazards into the hazard of NFF events and the consequences to safety, dependability, and cost are also discussed. It should be noted that the paper focuses on Built-in-Test (BIT), i.e., the capability of a test system of performing automatic fault recognition and fault localization. The hardware aspects, related to Built-in-Test Equipment (BITE), are not specifically considered. An earlier version of this paper has previously been presented at the IAI2023 Congress.</p>

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Event tree analysis for risk-based assessment of digitalised condition-based maintenance in railway

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Peter Söderholm,
  • Per-Anders Akersten

摘要

The purpose of this paper is to describe a risk-based scenario analysis, by use of an Event Tree Analysis approach, which will support testability considerations and test level integration in the design or dependability improvement phases of technical systems. The proposed scenario analysis includes fault recognition and fault localization efforts and their associated hazards of false alarms, unrecognized faults, and un-localized faults. The combination of these single hazards into the hazard of NFF events and the consequences to safety, dependability, and cost are also discussed. It should be noted that the paper focuses on Built-in-Test (BIT), i.e., the capability of a test system of performing automatic fault recognition and fault localization. The hardware aspects, related to Built-in-Test Equipment (BITE), are not specifically considered. An earlier version of this paper has previously been presented at the IAI2023 Congress.