Machine learning-based approach for online fault Diagnosis of Discrete Event System
This addresses the need for efficient, low-cost online diagnosis with fewer false alarms in industrial automation, though it appears incremental by applying existing ML methods to a known domain.
The paper tackles the problem of online fault diagnosis in Automated Production Systems modeled as Discrete Event Systems, proposing a machine learning-based multi-class classifier that predicts normal or faulty states and identifies specific faults.
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.