Fast feature selection using hybrid ranking and wrapper approach for automatic fault diagnosis of motorpumps based on vibration signals

This work presents a novel hybrid approach for feature selection using a combination of ranking and wrapper methods. Its main goal is to select features quickly, without significant loss of classification performance. Experiments comparing this approach with Sequential Forward Feature (SFS) selection showed its viability using Support Vector Machine and K-Nearest Neighbor classifiers in specific scenarios. As a test bed, vibrational signals were employed which need a previous feature extraction stage to create a classification system. In two experiments, 74 and 130 features were extracted from these databases. The proposed approach performed at least ten times faster than SFS, with 0.32% loss of accuracy in the worst case, requiring 26% to 57.5% less features to achieve its highest accuracy.