Automated real-time anomaly detection of temperature sensors through machine-learning
by Debanjana Nayak; Harry Perros
International Journal of Sensor Networks (IJSNET), Vol. 34, No. 3, 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.

Online publication date: Mon, 16-Nov-2020

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