Anomaly Detection Based on Aggregation of Indicators

Tsirizo Rabenoro (1, 2) , Jérôme Lacaille (2) , Marie Cottrell (1) , Fabrice Rossi (1)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 SNECMA Villaroche [Moissy-Cramayel]

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.

Mots clés: Anomaly Detection; Turbofan; Health Monitoring

Domaines: Machine Learning [stat.ML]

Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi. Anomaly Detection Based on Aggregation of Indicators. 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Jun 2014, Bruxelles, Belgium. pp.64-71. ⟨hal-01017853v2⟩