Open Access

ARTICLE

An Anomaly Detection Method of Industrial Data Based on Stacking Integration

Kunkun Wang^1,2, Xianda Liu^2,3,4,*

1 College of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
2 Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, 110016, China
3 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
4 Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

* Corresponding Author: Xianda Liu. Email:

Journal on Artificial Intelligence 2021, 3(1), 9-19. https://doi.org/10.32604/jai.2021.016706

Received 09 January 2021; Accepted 15 March 2021; Issue published 02 April 2021

Abstract

With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule tree method are adopted to reduce the dimension of the process data, which can restore the original feature information of the data to the maximum extent. Random forest (RF), Adaboost, XGboost, SVM were selected as the first layer of basic learners. Logistic regression (LR) was used as the secondary learner to build the exception detection model based on stacking integrated method. TE data was used to train the base learner model and the integrated model. By comparing and analyzing the experimental results of between integrated model and each basic learning model. By comparing and analyzing the experimental results of the constructed anomaly detection model and the basic learning model, the accuracy of process data anomaly detection is effectively improved, and the false alarm rate of process data anomaly detection is effectively reduced.

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Keywords

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