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Automated real-time anomaly detection of temperature sensors through machine-learning
- Source :
- International Journal of Sensor Networks; 2020, Vol. 34 Issue: 3 p137-152, 16p
- Publication Year :
- 2020
-
Abstract
- Fast identification of faulty sensors is necessary for guaranteeing their robust functions in diverse applications ranging from extreme weather prediction to energy saving to healthcare. We present an automated machine-learning based framework that can detect anomalies of temperature sensor data in real-time. We adopted a purely temporal approach that utilises a univariate time-series (UTS) generated by a single sensor. The framework divides the UTS into subsequences, models each subsequence stochastically as an autoregressive function, and finally mines the function parameters with a one-class support vector machines (OC-SVM) that classifies any outlier as an anomaly. Extensive experimentation showed that the framework identifies both normal and anomalous data correctly with high degrees of accuracy.
Details
- Language :
- English
- ISSN :
- 17481279 and 17481287
- Volume :
- 34
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- International Journal of Sensor Networks
- Publication Type :
- Periodical
- Accession number :
- ejs54649732
- Full Text :
- https://doi.org/10.1504/IJSNET.2020.111233