Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area.

中文翻译：

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智能制造中基于AI的监视的状态和重点部分介绍

在过去的几十年中，以人工智能（AI）技术为支撑的智能化已成为工业制造的重要趋势，从而加速了智能制造的发展。在现代工业中，标准AI具有其他属性，产生了所谓的工业人工智能（IAI），它已成为智能制造的技术核心。人工智能驱动的制造业在从制造流程到最终产品物流的闭环生产链的许多方面都带来了显着改善。尤其是，结合了领域知识的IAI极大地有益于生产监控领域。先进的AI方法，例如深度神经网络，对抗训练，迁移学习和转移学习已被广​​泛用于支持整个生产过程的诊断和预测性维护。人们普遍认为，IAI是推动工业制造业未来发展所需的关键技术。本文全面概述了AI驱动的制造及其在监控中的应用。更具体地说，它总结了IAI的关键技术，并针对生产监控的三个主要方面讨论了它们的典型应用场景：故障诊断，剩余使用寿命预测和质量检查。此外，还讨论了IAI存在的问题和未来的研究方向。本文通过将其编织成概述，进一步介绍了本节中有关智能制造中基于AI的监视的论文，