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Extension of the Learning Domain in Monitoring Turbofan Start Capability System

Abstract : The presented system monitors a turbofan start sequence using indicators and operating conditions to detect abnormal behavior. It is based on the analysis of the residuals between the measured indicator values and the corresponding estimated values assuming healthy state. Estimation uses regression models trained on a database. However, as in many monitoring problems, the amount of data is limited due to application issues and covers only a limited region of the feature space. Thus, the models are trained in a limited domain defined implicitly by the available learning data and their efficiency is not controlled outside this implicit domain. This paper deals with the definition and the extension of the models validity region while keeping the extension effect on the monitoring process under control. A methodology based on one-class SVM is proposed and is applied to the presented monitoring system. Practical and methodological conclusions are drawn from the proposed experiments.
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Contributor : Jean-Baptiste VU VAN Connect in order to contact the contributor
Submitted on : Tuesday, October 15, 2019 - 1:53:48 PM
Last modification on : Wednesday, August 31, 2022 - 6:55:32 PM


  • HAL Id : hal-02316545, version 1



Edith Grall-Maës, Pierre Beauseroy, Antoine Grall, Alexandre Ausloos, Jean-Rémi Massé. Extension of the Learning Domain in Monitoring Turbofan Start Capability System. International Journal of Performability Engineering, Totem Publisher, Inc., 2012, 8, pp.265 - 278. ⟨hal-02316545⟩



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