With the rapid development of information technologies, the computing, networking, and physical elements in industrial environments are becoming tightly amalgamated with each other, resulting in the formation of the so-called Industrial Cyber-Physical Systems (ICPS). These systems forge the core of current real-world networked industrial infrastructures, having a cyber-representation of physical assets through digitalization of data across the enterprise, along the value stream and process engineering life cycle, along the digital thread, and along the supply chain. Typical applications of ICPS include smart grids, digital factory, cognitive and collaborative robots, freight transportation, process control, plant-wide systems, medical monitoring, etc. ICPS often operate in an unpredictable and challenging environment, where various disturbances, such as unplanned natural events, human faults or malicious behaviors, software and hardware failures, etc., may occur during the automation process at runtime. Moreover, ICPS can exhibit strong reconfigurability and evolve structurally for many purposes. During this evolution, new and unforeseen possibilities in the service-oriented business process may appear among various ICPS components. In particular, new “emergent” behaviors may arise that need to be monitored, understood, managed and controlled. When there are significant uncertainties, such emergent behaviors could make the evolved ICPS unstable and unable to meet the quality/performance targets, even resulting in hazards. Well-designed machine-learning techniques have the potential to effectively address the uncertainties and disturbances in the automation of ICPS. They can also facilitate the automated discovery of valuable underlying rules and patterns to improve the performance of ICPS in all phases of their life cycles.

中文翻译：

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客座社论用于弹性工业网络物理系统的机器学习


随着信息技术的快速发展，工业环境中的计算、网络和物理元素日益紧密地融合在一起，形成了所谓的工业信息物理系统（ICPS）。这些系统构成了当前现实世界网络工业基础设施的核心，通过整个企业、沿着价值流和流程工程生命周期、沿着数字主线以及沿着供应链的数据数字化，拥有实物资产的网络表示。 。 ICPS的典型应用包括智能电网、数字工厂、认知和协作机器人、货运、过程控制、全厂系统、医疗监控等。ICPS经常在不可预测和具有挑战性的环境中运行，其中各种干扰，例如计划外的自然灾害自动化过程在运行时可能会发生事件、人为错误或恶意行为、软件和硬件故障等。此外，ICPS 可以表现出强大的可重构性，并为多种目的进行结构演化。在此演进过程中，各种 ICPS 组件中可能会出现面向服务的业务流程中新的和不可预见的可能性。特别是，可能会出现需要监视、理解、管理和控制的新“紧急”行为。当存在重大不确定性时，此类突发行为可能会使演进的ICPS不稳定，无法满足质量/性能目标，甚至导致危险。精心设计的机器学习技术有可能有效解决 ICPS 自动化中的不确定性和干扰。 它们还可以促进有价值的底层规则和模式的自动发现，以提高 ICPS 在其生命周期的所有阶段的性能。