Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine


  • Anis Mhalla Research Unit of Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir, University of Monastir, 5019, Ibn Eljazzar City, Monastir, Tunisia


Time constraints, Diagnosis, P-time Petri Nets, Chronicles, winding machine


This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined.


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How to Cite

A. Mhalla, “Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine”, Int J Eng and Appl Phys, vol. 1, no. 2, pp. 76–83, May 2021.