  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 tackling technical challenges that practitioners face when
detecting anomalies 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 our industrial partner, which we use here to show
its effectiveness. We also evaluate the performance of SAnD by comparing it
with a selection of semi-supervised methods on public datasets from the
literature on anomaly detection. We conclude that SAnD is effective, broadly
applicable, and outperforms existing approaches in both anomaly detection and
runtime.
