Accurate and fast anomaly detection in industrial processes and IoT environments

arXiv:2404.17925v1 Announce Type: new
Abstract: We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms and feature importance techniques. To our knowledge, SAnD is the first procedure that integrates these tools to identify anomalies and help decipher their putative causes. We show how each step contributes to …

abstract algorithms anomaly anomaly detection arxiv cs.lg detection environments filters industrial inflation iot novel processes sand semi-supervised simple stat.ap statistical threshold tools type variance