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.

The health ministry has unveiled a list of more than 10,000 medical clinics accepting new patients for online diagnoses in an effort to curb the spread of the novel coronavirus among doctors and patients. In online meetings with patients, doctors provide recommendations and diagnoses remotely through technology such as smartphones. The method is said to be effective in protecting the medical system from the dangers of increased infections inside health facilities. The ministry said Friday that it will update the list of clinics providing telemedicine for first-time patients as it receives reports from local governments across the country. Amid the coronavirus pandemic, the ministry has modified its stance that the first consultation with each patient should be conducted face-to-face.

This document demonstrates that the efficient approach for diagnosis of Petri nets via integer linear programming may be unable to detect a fault even if the system is diagnosable.