Abstract
Abstract 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 l 1 -penalty with the squared l 2 -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.
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