A1330
Title: Accurate and fast anomaly detection in industrial processes
Authors: Simone Tonini - Sant Anna School of Advanced Studies - Pisa (Italy) [presenting]
Andrea Vandin - Sant Anna School for Advanced Studies - Pisa (Italy)
Francesca Chiaromonte - Scuola Superiore SantAnna (Italy)
Daniele Licari - Sant Anna School for Advanced Studies - Pisa (Italy)
Fernando Barsacchi - ACelli Group - Lucca (Italy)
Abstract: The purpose is to present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in data from industrial processes, 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 knowledge, SAnD is the first procedure that integrates these tools to identify anomalies and help decipher their putative causes. How each step contributes to tackling technical challenges that practitioners face when detecting anomalies is shown in industrial contexts, where signals can be highly multicollinear, have unknown distributions, and intertwine short-lived noise with the long(er)-lived actual anomalies. The development of SAnD was motivated by a concrete case study from the industrial partner, which is used to show its effectiveness. The performance of SAnD is also evaluated by comparing it with a selection of semi-supervised methods on public datasets from the literature on anomaly detection. SAnD is concluded to be effective, broadly applicable, and outperforms existing approaches in both anomaly detection and runtime.
