Railway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.

由于运行中的可靠性故障，铁路运输系统（RTS）故障对最终用户和运营商造成巨大压力。尽管对提高RTS的可靠性（例如信号，轨道和基础结构）进行了广泛的研究，但很少有人尝试开发有效的优化模型来提高可靠性和维护机车车辆子系统。本文提出了一种融合了可靠性，风险和维护技术的新混合模型，以促进工程故障和资产管理决策分析。该模型的上游部分包括风险和可靠性技术，分别用于使用故障模式影响，关键性分析和故障树分析进行自下而上和自上而下的故障分析。使用分析层次结构过程的多准则成对比较功能，将DMG图的最差区域。通过案例研究说明了混合模型，该案例使用来自英国的火车运营商的运行数据替换了不可靠的气动制动单元（PBU），故障频率和延迟分钟数分别超过了运营商最初的目标300％和900％ 。结果表明，新型混合模型可以有效地分析和识别满足操作员的可靠性，风险和维护要求的新的PBU子系统。