Artificial Intelligence Enabled Digital Twin For Predictive Maintenance in Industrial Automation System: A Novel Framework and Case Study

M. Siddiqui, G. Kahandawa, H. Hewawasam
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引用次数: 2

Abstract

Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions.
基于人工智能的工业自动化系统预测性维护数字孪生:一个新的框架和案例研究
工业自动化系统在先进制造环境中被过度使用。这些系统总是容易发生故障,这不仅会干扰顺利的制造操作,还会对操作人员造成伤害。因此,本研究提出了一种新的预测性维护算法,可用于检测自动化系统中的异常,以避免资产故障。使用人工智能支持的数字孪生模型来检测早期异常,以避免设备故障的灾难性影响。利用实时传感器数据验证了该算法的有效性。数据通过安装在物理系统上的传感器记录下来。本文介绍了该算法在工业自动化系统故障状态下检测异常的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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