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Article Dans Une Revue Aerospace Science and Technology Année : 2019

Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

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Résumé

The objective of this work is the development of a fault diagnostic system for a shaker blower used in on-board aeronautical systems. Features extracted from condition monitoring signals and selected by the ELastic NET (ELNET) algorithm, which combines -penalty with the squared -penalty on model parameters, are used as inputs of a Multinomial Logistic regression (MLR) model. For validation, the developed approach is applied to experimental data acquired on a shaker blower system (as representative of aeronautical on-board systems) and on three additional experimental datasets of literature. The satisfactory diagnostic performances obtained show the potential of the method for developing sound diagnostic classifiers from a very large set of features, even when only few training data are available.

Dates et versions

hal-02432662 , version 1 (08-01-2020)

Identifiants

Citer

F. Cannarile, M. Compare, P. Baraldi, G. Diodati, V. Quaranta, et al.. Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems. Aerospace Science and Technology, 2019, 94, pp.105392. ⟨10.1016/j.ast.2019.105392⟩. ⟨hal-02432662⟩
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