In recent years, more and more manufacturers and operators of fleets of mobile systems have been focusing their efforts on studying and developing conditional maintenance, monitoring, and diagnostic strategies to cope with an increasingly competitive, unstable, costly, and unpredictable environment. This paper proposes a case study concerning the application of a novel event management architecture, called EMH², to a fleet of trains. This EMH² architecture, which applies the holonic paradigm, aims to facilitate the monitoring and diagnosis of a fleet of mobile systems. It is based on a recursive decomposition of cooperative monitoring holons. The definition of a generic event modeling, called SurfEvent, is the second key element of the contribution. EMH² has been designed to be applicable to any kind of system or equipment up to fleet level. The edge computing paradigm has been adopted for implementation purpose. The EMH² architecture is designed to facilitate asynchronous and progressive onboard and off-board deployments. A real-world application of EMH² to a fleet of ten trains currently in use, in collaboration with our industrial partner, Bombardier Transport, is presented. Three key performances indicators have been estimated by comparing EMH² with the current industrial situation. These indicators are (1) the number of fleet maintenance visits, (2) the time needed by a maintenance operator to investigate and diagnose, and (3) the time needed by the system to update data regarding the health status and monitoring of trains. Results obtained outperformed industrial expectations. The paper finally discusses feedbacks from experience and limitations of the work.

中文翻译：

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用于监视和诊断列车车队的事件管理架构：一个案例研究

近年来，越来越多的移动系统制造商和运营商一直致力于研究和开发条件维护，监视和诊断策略，以应对竞争日益激烈，不稳定，成本高昂且不可预测的环境。本文提出了一个案例研究，该案例涉及一种称为EMH ²的新颖事件管理架构在火车车队中的应用。此EMH ²体系结构采用了完整的范例，旨在促进对移动系统机群的监视和诊断。它基于协作监视子的递归分解。通用事件建模的定义（称为SurfEvent）是贡献的第二个关键要素。EMH ²已被设计为适用于机队级别以下的任何类型的系统或设备。边缘计算范例已被用于实现目的。EMH ²体系结构旨在促进异步和渐进式板载和板外部署。与我们的工业合作伙伴庞巴迪运输公司合作，将EMH ²实际应用到目前使用的十列火车车队中。通过比较EMH ²估算了三个关键绩效指标与当前的工业形势。这些指标包括：（1）车队维护访问的次数；（2）维护操作员进行调查和诊断所需的时间；（3）系统更新有关健康状况和列车监控的数据所需的时间。获得的结果优于行业预期。本文最后讨论了工作经验和局限性的反馈。