论文标题
基于机器学习的方法用于在线故障诊断离散事件系统
Machine learning-based approach for online fault Diagnosis of Discrete Event System
论文作者
论文摘要
本文考虑的问题是对自动化生产系统的在线诊断，其传感器和执行器提供了离散的二元信号，这些信号可以建模为离散事件系统。即使有许多诊断方法，它们都无法满足实施有效诊断系统的所有标准（例如智能解决方案，平均努力，合理的成本，在线诊断，较少的错误警报等）。此外，这些技术需要系统的正确，健壮和代表性的模型，或者需要连续更新的相关数据或专家知识。在本文中，我们提出了一种基于机器学习的诊断系统的方法。它被认为是预测植物状态的多级分类器：正常或故障以及在行为失败的情况下出现了什么故障。
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.
