Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr 30:2022:8568917.
doi: 10.1155/2022/8568917. eCollection 2022.

Online Anomaly Detection of Industrial IoT Based on Hybrid Machine Learning Architecture

Jia Guo  1 , Yue Shen  2
Affiliations

Online Anomaly Detection of Industrial IoT Based on Hybrid Machine Learning Architecture

Jia Guo et al. Comput Intell Neurosci. .

Retraction in

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Depiction of the data set (random state).
Figure 2
Figure 2
Flowchart of the proposed anomaly detection process.
See this image and copyright information in PMC

References

    1. Banafa A. Secure and Smart Internet of Things (IoT): Using Blockchain and AI . Denmark: River Publishers; 2018. 2 the industrial Internet of Things (IIoT): challenges, requirements and benefits; pp. 7–12.
    1. Schneider S. Internet of Things and Data Analytics Handbook . Hoboken, New Jersey, U.S: Wiley; 2017. THE INDUSTRIAL INTERNET OF THINGS (IIoT) pp. 41–81. - DOI
    1. Shah G., Tiwari A. Anomaly detection in IIoT. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data; January. 2018; Goa India. pp. 295–300. - DOI
    1. Zhou L., Guo H. Anomaly detection methods for IIoT networks. Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI); July. 2018; Singapore. pp. 214–219. - DOI
    1. Mumtaz S., Alsohaily A., Pang Z., Rayes A., Tsang K. F., Rodriguez J. Massive Internet of Things for industrial applications: addressing wireless IIoT connectivity challenges and ecosystem fragmentation. IEEE Industrial Electronics Magazine . 2017;11(1):28–33. doi: 10.1109/MIE.2016.2618724. - DOI

Publication types

LinkOut - more resources