Low-Pass Filter Application for Anomaly Detection with Sparse Autoencoder

Anomaly detection (AD) techniques are adopted to identify instances with patterns that significantly differ from the general behavior of a dataset. The development of new techniques, such as those based on deep learning, and the higher data availability have increased the use of AD techniques in challenging tasks such as in the detection of failures in industrial equipment’s. Generally, an AD technique generates an anomaly score for each instance, later used to classify it as anomalous or normal, based on a threshold above which the instance is considered anomalous. A problem that is commonly observed in practice is the presence of spurious peaks in the anomaly score signal and other irregularities that may cause, for example, a high number of false positives in AD. In this paper, we investigated the use of low-pass filters in order to smooth the anomaly scores derived by a Sparse Autoencoder (SAE) model adopted for AD. In our experiments, we investigated the usefulness of the low-pass filters considering two different approaches: (1) directly applied on the anomaly scores; and (2) applied on the classification signal returned by the AD model. The experiments were performed on a case study of AD in a metro’s air production unit. Generally, the filter applied directly on the anomaly score maximized true positives. In turn, the filter applied after classification minimized false positives. It was observed that in general the use of LPF was essential to detect sequences of anomalies. Thus, how to apply low-pass filters in AD must be defined according to specific application goals.