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.
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Bellas, A., Bouveyron, C., Cottrell, M., Lacaille, J. (2014). Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_14
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DOI: https://doi.org/10.1007/978-3-319-07695-9_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
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