An Overproduce-and-Choose Strategy to Create Classifier Ensembles with Tuned SVM Parameters Applied to Real-World Fault Diagnosis

We present a supervised learning classification method for model-free fault detection and diagnosis, aiming to improve the maintenance quality of motor pumps installed on oil rigs. We investigate our generic fault diagnosis method on 2000 examples of real-world vibrational signals obtained from operational faulty industrial machines. The diagnostic system detects each considered fault in an input pattern using an ensemble of classifiers, which is composed of accurate classifiers that differ on their predictions as much as possible. The ensemble is built by first using complementary feature selection techniques to produce a set of candidate classifiers, and finally selecting an optimized subset of them to compose the ensemble. We propose a novel ensemble creation method based on feature selection. We work with Support Vector Machine (SVM) classifiers. As the performance of a SVM strictly depends on its hyperparameters, we also study whether and how varying the SVM hyperparameters might increase the ensemble accuracy. Our experiments show the usefulness of appropriately tuning the SVM hyperparameters in order to increase the ensemble diversity and accuracy.