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Event management architecture for the monitoring and diagnosis of a fleet of trains: a case study

Abstract : 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.
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https://hal-uphf.archives-ouvertes.fr/hal-03426305
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Submitted on : Friday, November 12, 2021 - 10:41:27 AM
Last modification on : Saturday, November 13, 2021 - 3:53:18 AM

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Ahmat Fadil Adoum, Damien Trentesaux, Guillaume Branger. Event management architecture for the monitoring and diagnosis of a fleet of trains: a case study. Journal of Modern Transportation, 2019, 27 (3), pp.169-187. ⟨10.1007/s40534-019-0187-0⟩. ⟨hal-03426305⟩

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