Multi-View Feature Construction Using Genetic Programming for Rolling Bearing Fault Diagnosis [Application Notes

Abstract

Rolling bearing fault diagnosis is an important task in mechanical engineering. Existing methods have several limitations, such as requiring domain knowledge and a large number of training samples. To address these limitations, this paper proposes a new diagnosis approach, i.e., multiview feature construction based on genetic programming with the idea of ensemble learning (MFCGPE), to automatically construct high-level features from multiple views and build an effective ensemble for identifying different fault types using a small number of training samples. The MFCGPE approach uses a new program structure to automatically construct a flexible number of features from every single view. A new fitness function based on accuracy and distance is developed in MFCGPE to improve the discriminability of the constructed features. To further improve the generalization performance, an ensemble of classifiers based on k-nearest neighbor is created by using the constructed features from every single view. Three bearing datasets and 19 competitive methods are used to validate the effectiveness of the new approach. The results show that MFCGPE achieves higher diagnostic accuracy than all the compared methods on the three datasets with a small number of training samples.