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

  • Anastasios Bellas
  • Charles Bouveyron
  • Marie Cottrell
  • Jerome Lacaille
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 295)

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.

Keywords

Health Monitoring aircraft SOM clustering anomaly detection confidence intervals 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anastasios Bellas
    • 1
  • Charles Bouveyron
    • 2
  • Marie Cottrell
    • 1
  • Jerome Lacaille
    • 3
  1. 1.SAMM, Université Paris 1 Panthé on-SorbonneParisFrance
  2. 2.Laboratoire MAP5Université Paris Descartes & Sorbonne Paris CitéParisFrance
  3. 3.SNECMA, Rond-Point René Ravaud-RéauMoissy-Cramayel CEDEXFrance

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