Real-time anomaly detection in gas sensor streaming data
Haibo Wu, liang Shi · International Journal of Embedded Systems · 2020
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.