Automatic Acquisition and Update of a Causal Temporal Signatures Base- for Faults Diagnosis in Automated Production Systems

Nourhène Ben Rabah (1, 2) , Ramla Saddem (3) , Faten Ben Hmida (4) , Véronique Carré-Ménétrier (1) , Moncef Tagina (5)

1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
2 URCA - Université de Reims Champagne-Ardenne
3 STF - Systèmes Tolérants aux Fautes
4 IMS - Laboratoire de l'intégration, du matériau au système
5 SOIE - Stratégies d’Optimisation et Informatique intelligentE

Causal Temporal Signatures (CTS) is an efficient formalism for behaviors description and recognition of fault diagnosis in Discrete Event Systems (DES). The main advantages of this formalism are the readability and the expressivity. Indeed, it is able to describe clearly all desired behaviors and it is understandable and readable by an expert in the field. However, it raises the problem of acquisition and updating of expert knowledge stored in a CTS base. In this paper, we suggest an incremental learning approach based on the simulation to acquire and update automatically a consistent CTS base. The proposed approach is illustrated with an example applied to the turntable helps to understand the different modules of the method.

Mots clés: Similarity; Learning; Automated Production System; Causal Temporal Signatures; Faults Diagnosis; Simulation; Discrete Event Systems

Domaines: Informatique [cs]; Automatique; Intelligence artificielle [cs.AI]

Nourhène Ben Rabah, Ramla Saddem, Faten Ben Hmida, Véronique Carré-Ménétrier, Moncef Tagina. Automatic Acquisition and Update of a Causal Temporal Signatures Base- for Faults Diagnosis in Automated Production Systems. International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2017, Madrid, Spain. pp.262-269, ⟨10.5220/0006430102620269⟩. ⟨hal-01899937⟩