Title: Real-time anomaly detection in gas sensor streaming data

Authors: Haibo Wu; Shiliang Shi

Addresses: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China; Hunan Province Key Laboratory of Safe Mining Techniques of Coal Mines, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China ' School of Resource and Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan Hunan, 411201, China

Abstract: In order to improve the timeliness and accuracy of coal mine gas disaster risk assessments, it is important to detect anomalies in the gas sensor streaming data in real-time. In this paper, the support vector regression (SVR) algorithm combined with the normal statistical distribution technique is used to establish a real-time anomaly detection model for gas sensor streaming data. Furthermore, a prototype system for the real-time anomaly detection in gas sensor streaming data that is built using the stream processing framework Spark Streaming is presented. Experiments show that the real-time anomaly detection system can periodically update the anomaly detection model and determine anomalies in the sensor streaming data in real-time. For a window size of 9, an update cycle of 1 and an anomaly threshold of 0.95, the anomaly detection model is better than the boxplot, the statistical analysis and the clustering algorithm regarding the prediction precision and accuracy.

Keywords: gas sensor; anomaly detection; streaming data; SVR; Spark Streaming.

DOI: 10.1504/IJES.2021.111985

International Journal of Embedded Systems, 2021 Vol.14 No.1, pp.81 - 88

Received: 12 Oct 2019
Accepted: 07 Nov 2019
Published online: 22 Dec 2020 *

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