Online Anomaly Detection of Industrial IoT Based on Hybrid Machine Learning Architecture. Guo, J. & Shen, Y. Computational Intelligence and Neuroscience, 2022:e8568917, April, 2022. Publisher: Hindawi![Online Anomaly Detection of Industrial IoT Based on Hybrid Machine Learning Architecture [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{guo_online_2022,
title = {Online {Anomaly} {Detection} of {Industrial} {IoT} {Based} on {Hybrid} {Machine} {Learning} {Architecture}},
volume = {2022},
issn = {1687-5265},
url = {https://www.hindawi.com/journals/cin/2022/8568917/},
doi = {10.1155/2022/8568917},
abstract = {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.},
language = {en},
urldate = {2022-05-03},
journal = {Computational Intelligence and Neuroscience},
author = {Guo, Jia and Shen, Yue},
month = apr,
year = {2022},
note = {Publisher: Hindawi},
pages = {e8568917},
}
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