Canonical Correlation Based Feature Extraction with Application to Anomaly Detection in Electric Appliances

This paper proposes a canonical correlation based feature extraction method with application to anomaly detection in electric appliances. Electric appliances in homes, offices or manufacturing factories are nowadays monitored by Internet of Things (IoT) platforms and systems. For unsupervised anomaly detection in such IoT systems, learning a model is challenging, since normal and anomaly behavior coexist in time-domain signals and are difficult to identify. For accurate model training, we propose to split odd and even frequency harmonics of electric current signals and transform using canonical correlation analysis to extract discriminative features. Evaluations on real-world data demonstrates that proposed approach outperforms the conventional unsupervised feature extraction methods.