Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

Ming Wan1, Jinfang Li1, Jiangyuan Yao2, *, Rongbing Wang1, 3, Hao Luo1

1 School of Information, Liaoning University, Shenyang, 110036, China.
2 School of Computer Science & Cyberspace Security, Hainan University, Haikou, 570228, China.
3 School of Informatics, Edinburgh University, Edinburgh, EH89AB, UK.

* Corresponding Author: Jiangyuan Yao. Email: email.

Computers, Materials & Continua 2020, 63(3), 1415-1431. https://doi.org/10.32604/cmc.2020.09692

Abstract

In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) respectively, are introduced as alternative detection engines to match with our feature extraction approach. All experimental results clearly show that the proposed feature extraction approach can effectively coordinate with the optimized classification algorithms, and the optimized GA-BPNN classifier is suggested as a more applicable detection engine by comparing its average detection accuracies with the ones of PSOOCSVM classifier.

Keywords


Cite This Article

M. Wan, J. Li, J. Yao, R. Wang and H. Luo, "State-based control feature extraction for effective anomaly detection in process industries," Computers, Materials & Continua, vol. 63, no.3, pp. 1415–1431, 2020.

Citations




cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2242

    View

  • 1328

    Download

  • 0

    Like

Share Link