The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and the conventional centralized cloud computing-based IoT solutions always lead to a heavy burden on the network bandwidth due to the large amount of sensor data collected frequently that has to be transmitted to the central server and this leads to poor response time for the monitoring system. To address this problem, this article develops an artificial intelligence-assisted distributed system for manufacturing plant-wide predictive maintenance applications. The developed distributed system relies on the feature selection technique to identity an optimal feature subset for each type of fault and is enabled by deploying each independent model built on the obtained feature subset into different edge nodes. The distributed approach enables the data to be processed near the sensors, requiring less data to be transmitted to the central cloud server reducing network delay and delivering more accurate results. In addition, our proposed feature selection approach is especially designed to accommodate the characteristics of IIoT data such as the lack of labels. The effectiveness of the proposed method is validated using the widely used public Tennessee Eastman dataset.

中文翻译：

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使用 AI 为 IoT 预测性维护解决方案提供支持：用于制造工厂范围监控的分布式系统

工业 4.0 的出现和工业物联网 (IIoT) 的快速发展，通过在基于物联网的预测性维护系统中部署自动故障检测，为制造商提供了远程监控过程的能力。然而，监控目标现在是全厂范围内的制造，而不仅仅是局部区域。涉及多种类型的故障，传统的基于集中式云计算的物联网解决方案由于频繁采集的大量传感器数据需要传输到中央服务器，导致网络带宽负担过重，导致性能不佳监控系统的响应时间。为了解决这个问题，本文开发了一个人工智能辅助的分布式系统，用于制造工厂范围的预测性维护应用程序。开发的分布式系统依赖于特征选择技术来为每种类型的故障识别最佳特征子集，并通过将建立在获得的特征子集上的每个独立模型部署到不同的边缘节点来实现。分布式方法使数据能够在传感器附近进行处理，需要将更少的数据传输到中央云服务器，从而减少网络延迟并提供更准确的结果。此外，我们提出的特征选择方法是专门为适应 IIoT 数据的特征而设计的，例如缺少标签。使用广泛使用的公共田纳西伊士曼数据集验证了所提出方法的有效性。