Zhengzhou Sias University, Department of Electronics and Information, Zhengzhou 451100, China.
Henan Geology Mineral College, Zhengzhou 451464, China.
Comput Intell Neurosci. 2022 Apr 30;2022:8568917. doi: 10.1155/2022/8568917. eCollection 2022.
Industrial IoT (IIoT) in Industry 4.0 integrates everything at the level of information technology with the level of technology of operation and aims to improve Business to Business (B2B) services (from production to public services). It includes Machine to Machine (M2M) interaction either for process control (e.g., factory processes, fleet tracking) or as part of self-organizing cyber-physical distributed control systems without human intervention. A critical factor in completing the abovementioned actions is the development of intelligent software systems in the context of automatic control of the business environment, with the ability to analyze in real-time the existing equipment through the available interfaces (hardware-in-the-loop). In this spirit, this paper presents an advanced intelligent approach to real-time monitoring of the operation of industrial equipment. A hybrid novel methodology that combines memory neural networks is used, and Bayesian methods that examine a variety of characteristic quantities of vibration signals that are exported in the field of time, with the aim of real-time detection of abnormalities in active IIoT equipment are also used.
工业物联网 (IIoT) 在工业 4.0 中将信息技术层面与运营技术层面的所有内容集成在一起，旨在改善企业对企业 (B2B) 服务（从生产到公共服务）。它包括机器对机器 (M2M) 交互，用于过程控制（例如，工厂流程、车队跟踪），或作为无需人工干预的自组织网络物理分布式控制系统的一部分。完成上述操作的一个关键因素是在业务环境的自动控制背景下开发智能软件系统，该系统具有通过现有接口（硬件在环）实时分析现有设备的能力。本着这种精神，本文提出了一种用于实时监测工业设备运行的先进智能方法。使用了一种混合的新型方法，结合了记忆神经网络和贝叶斯方法，检查在时间域中导出的各种振动信号的特征量，旨在实时检测主动工业物联网设备中的异常情况。
