Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring

TL;DR

This paper presents a novel anomaly detection method using Self-Organizing Maps (SOM) with confidence intervals, tailored for health monitoring, by correcting for external variables and avoiding distributional assumptions.

Contribution

It introduces a cluster-specific correction model and a distance-based detection approach that improves accuracy over traditional methods in anomaly detection tasks.

Findings

Effective detection of aircraft engine anomalies

No assumption of data distribution required

Improved accuracy with cluster-specific correction

Abstract

We develop an application of SOM for the task of anomaly detection and visualization. To remove the effect of exogenous independent variables, we use a correction model which is more accurate than the usual one, since we apply different linear models in each cluster of context. We do not assume any particular probability distribution of the data and the detection method is based on the distance of new data to the Kohonen map learned with corrected healthy data. We apply the proposed method to the detection of aircraft engine anomalies.