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

被引：6

作者：

Bellas, Anastasios ^{[1
]}

Bouveyron, Charles ^{[2
,3
]}

Cottrell, Marie ^{[1
]}

Lacaille, Jerome ^{[4
]}

机构：

[1] Univ Paris 1 Panthe On Sorbonne, SAMM, 90 Rue Tolbiac, F-75013 Paris, France

[2] Univ Paris 05, Lab MAP5, F-75006 Paris, France

[3] Sorbonne Paris Cite, F-75006 Paris, France

[4] SNECMA, F-77550 Moissy Cramayel, France

来源：

ADVANCES IN SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION | 2014年 / 295卷

关键词：

Health Monitoring; aircraft; SOM; clustering; anomaly detection; confidence intervals; NOVELTY DETECTION;

D O I：

10.1007/978-3-319-07695-9_14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：145 / 155

页数：11

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