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International Journal of Crowd Science 2019, 3(3): 333-347 https://doi.org/10.1108/IJCS-09-2019-0024
Research paper | Open Access | Issue | Published: 09 December 2019

An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

Show Author's Information Xudong Lu1,  Shipeng Wang2,  Fengjian Kang1,  Shijun Liu3,  Hui Li4,  Xiangzhen Xu4,  Lizhen Cui4( )
School of Software, Shandong University, Jinan, China
School of Software, Research Center of Software and Data Engineering, Shandong University, Jinan, China
School of Computer Science and Technology, Shandong University, Jinan, China
Shandong University, Jinan, China
Keywords:
Clustering, Internet of things, Anomaly detection, Intelligent level
Cite this article:
Lu X, Wang S, Kang F, et al. An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models. International Journal of Crowd Science, 2019, 3(3): 333-347. https://doi.org/10.1108/IJCS-09-2019-0024
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Abstract Full text About this article
Purpose

The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge.

Design/methodology/approach

In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized.

Findings

The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences.

Originality/value

The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.


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About this article

An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

Show Author's information Xudong Lu1, Shipeng Wang2, Fengjian Kang1, Shijun Liu3, Hui Li4, Xiangzhen Xu4, Lizhen Cui4( )
School of Software, Shandong University, Jinan, China
School of Software, Research Center of Software and Data Engineering, Shandong University, Jinan, China
School of Computer Science and Technology, Shandong University, Jinan, China
Shandong University, Jinan, China

Abstract

Purpose

The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge.

Design/methodology/approach

In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized.

Findings

The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences.

Originality/value

The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.

Keywords: Clustering, Internet of things, Anomaly detection, Intelligent level

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Publication history
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Publication history

Received: 17 September 2019
Revised: 13 October 2019
Accepted: 16 October 2019
Published: 09 December 2019
Issue date: December 2019

Copyright

© The author(s)

Acknowledgements

Acknowledgements

This work is partially supported by National Key R&D Program No.2017YFB1400100.

Rights and permissions

Xudong Lu, Shipeng Wang, Fengjian Kang, Shijun Liu, Hui Li, Xiangzhen Xu and Lizhen Cui. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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