  The problem considered in this paper is the online diagnosis of Automated
Production Systems with sensors and actuators delivering discrete binary
signals that can be modeled as Discrete Event Systems. Even though there are
numerous diagnosis methods, none of them can meet all the criteria of
implementing an efficient diagnosis system (such as an intelligent solution, an
average effort, a reasonable cost, an online diagnosis, fewer false alarms,
etc.). In addition, these techniques require either a correct, robust, and
representative model of the system or relevant data or experts' knowledge that
require continuous updates. In this paper, we propose a Machine Learning-based
approach of a diagnostic system. It is considered as a multi-class classifier
that predicts the plant state: normal or faulty and what fault that has arisen
in the case of failing behavior.
