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To ensure secure and efficient transportation, railway vehicles require high levels of reliability, availability, maintainability, and safety (RAMS). These attributes are strongly influenced by the effectiveness of maintenance strategies. Due to rapid advancements in digitalization and automation, rolling stock maintenance strategies currently undergo a shift from traditional corrective and preventive methods to condition-based and predictive approaches. However, one of the main challenges in adopting condition-based maintenance (CBM) lies in the significant initial investment required. To reduce maintenance costs and maximize operational value, it is crucial to identify the subsystems, those are most prone to failure, as they can lead to unplanned downtime or safety-critical incidents. This paper analyzes maintenance logs and accident reports of passenger trains to identify recurrent failures and critical subsystems. The most failure-prone subsystems, which account for up to 80% of failures, are presented. Furthermore, the paper presents a comprehensive overview of the state-of-the-art CBM technologies used in railway vehicles, including both OnBoard systems and WaySide monitoring solutions. These findings support the development of more targeted and cost-efficient maintenance practices in the railway sector. This work was carried out within the framework of the research project “Minimum Sensor Equipment of Passenger Coaches, Multiple Units and Locomotives for Effective and Economic Condition Monitoring” on behalf of the German Centre for Rail Traffic Research (DZSF).

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Condition-Based Monitoring in Passenger Trains: Insights from Maintenance Logs and Accident Data

  • 会议论文

  • 发表日期：

    2026年

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  • Wiryanto Dharmawan,
  • Anton Beuss,
  • Raoul Schild,
  • Markus Hecht,
  • Beate Bender

摘要

To ensure secure and efficient transportation, railway vehicles require high levels of reliability, availability, maintainability, and safety (RAMS). These attributes are strongly influenced by the effectiveness of maintenance strategies. Due to rapid advancements in digitalization and automation, rolling stock maintenance strategies currently undergo a shift from traditional corrective and preventive methods to condition-based and predictive approaches. However, one of the main challenges in adopting condition-based maintenance (CBM) lies in the significant initial investment required. To reduce maintenance costs and maximize operational value, it is crucial to identify the subsystems, those are most prone to failure, as they can lead to unplanned downtime or safety-critical incidents. This paper analyzes maintenance logs and accident reports of passenger trains to identify recurrent failures and critical subsystems. The most failure-prone subsystems, which account for up to 80% of failures, are presented. Furthermore, the paper presents a comprehensive overview of the state-of-the-art CBM technologies used in railway vehicles, including both OnBoard systems and WaySide monitoring solutions. These findings support the development of more targeted and cost-efficient maintenance practices in the railway sector. This work was carried out within the framework of the research project “Minimum Sensor Equipment of Passenger Coaches, Multiple Units and Locomotives for Effective and Economic Condition Monitoring” on behalf of the German Centre for Rail Traffic Research (DZSF).