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Toward Digital Twin for Cyber Physical Production Systems Maintenance: Observation Framework Based on Artificial Intelligence Techniques

机译:对于网络物理生产系统的数字双胞胎维护:基于人工智能技术的观察框架

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Manufacturing Systems are considered complex engineering systems given the large number of integrated entities and their interactions. Unplanned events and disruptions that can happen at any time in real-word industrial environments increase the complexity of manufacturing production systems. In the fourth industrial revolution (so called Industry 4.0), the industrial sector is rapidly changing with emerging technologies like Cyber-Physical Production System (CPPS), Internet of Thing (IoT), Artificial Intelligence (AI), etc. However, the efficiency and reliability of these systems are still questionable in many circumstances. To address this challenge, an observation framework based on AI techniques aimed at elaborating predictive and reactive planning of the maintenance operations of CPPS is proposed in this paper. The proposed tool aims to improve the system's reliability and helps the maintenance supervisors to adjust maintenance decisions. In order to assess the performance of the proposed tool, a case study on an industry-type learning factory is considered. A proof of concept shows the efficiency of the framework.
机译:由于大量综合实体及其交互,制造系统被认为是复杂的工程系统。在真实的工业环境中随时发生的意外事件和中断提高了制造生产系统的复杂性。在第四次工业革命(所谓的行业4.0)中,工业部门随着网络 - 物理生产系统(CPPS),物联网(物联网),人工智能(AI)等的新兴技术迅速变化。但是,效率在许多情况下,这些系统的可靠性仍然是可疑的。为了解决这一挑战,提出了一种基于AI技术的观察框架,旨在旨在阐述CPP的维护操作的预测和无功规划。拟议的工具旨在提高系统的可靠性,并帮助维护监督员调整维护决策。为了评估所提出的工具的表现,考虑了对行业型学习工厂的案例研究。概念证明显示了框架的效率。

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