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Communication Dans Un Congrès Année : 2022

Timed automaton models for fault diagnosis of the drilling process on a CNC machine

Anna Carla Araujo
Yann Landon

Résumé

Fault diagnosis is a crucial task to guarantee reliability, and reduce losses and production cost in smart machining. In order to do so, it is necessary to implement a fault diagnoser that does not use a large amount of memory and that is capable of detecting the occurrence of a fault in a fast manner. In this paper, we propose timed automaton models, which represent the drilling process on a CNC machine, that are suitable for fault diagnosis. The fault diagnoser uses only the spindle power and Z axis displacement read directly from the system controller, and does not need any additional sensors. The proposed diagnoser is capable of identifying the occurrence of a wrong sequence of events executed by the system and the use of a material different from the expected.
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Dates et versions

hal-03640305 , version 1 (13-04-2022)
hal-03640305 , version 2 (21-06-2022)

Identifiants

  • HAL Id : hal-03640305 , version 1

Citer

M Moreira, Anna Carla Araujo, Yann Landon. Timed automaton models for fault diagnosis of the drilling process on a CNC machine. Congresso Brasileiro de Automação, 2022, Fortaleza, Brazil. ⟨hal-03640305v1⟩
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