Diagnosis and Treatment of Faults in Productive Systems based on Bayesian Networks and Petri Net

In this paper, we introduce a procedure for diagnosis and treatment of faults in productive systems, i.e., a supervision strategy that considers not only the normal behavior of system's components, but also abnormal (faulty) conditions of them. The present approach uses Bayesian networks for the diagnosis and decision-making purposes, and Petri net for the synthesis, modeling and control purposes. The integration of these techniques guarantees the specified functionality of the system. Special emphasis is laid on methodological issues and industrial systems, where a hierarchical structure can be adopted. It is presented, as a case study, the material entry system of a continuous pickling line process of a steel industry.