Anomaly Detection Based on Aggregation of Indicators
This work addresses anomaly detection and classification for human operators in monitoring systems, but it is incremental as it builds on existing methods like Naive Bayes and feature selection.
The paper tackled the problem of automatic anomaly detection and identification of problem origins by introducing a methodology that aggregates expert knowledge through a large number of indicators, using feature selection and a Naive Bayes classifier, with parameters optimized indirectly, and tested on simulated data.
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.