Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Online annotation and prediction for regime switching data streams
Proceedings of the 2009 ACM symposium on Applied Computing
Fault Prediction in Aircraft Engines Using Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Aircraft engine fleet monitoring using self-organizing maps and edit distance
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Patient's motion recognition based on SOM-decision tree
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
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Aircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficients procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a meaningful way successive data measured on aircraft engines. The core of the procedure is based on Self-Organizing Maps (SOM) which are used to visualize the evolution of the data measured on the engines. Rough measurements can not be directly used as inputs, because they are influenced by external conditions. A preprocessing procedure is set up to extract meaningful information and remove uninteresting variations due to change of environmental conditions. The proposed procedure contains three main modules to tackle these difficulties: environmental conditions normalization (ECN), change detection and adaptive signal modeling (CD) and finally visualization with Self-Organizing Maps (SOM). The architecture of the procedure and of modules are described in details in this paper and results on real data are also supplied.