A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

Abstract

Intelligent bearing fault diagnosis has received much research attention in the field of rotary machinery systems where miscellaneous deep learning methods are generally applied. Among these methods, convolution neural network is particularly powerful because of its ability to learn fruitful features from the original data. However, normal convolutions cannot fully utilize the information along the data flow while the features are being abstracted in deeper layers. To address this problem, a new supervised learning model is proposed for small sample size bearing fault diagnosis with consideration of imbalanced data. This model, which is developed based on a convolution neural network, has a high generalization ability, and its performance is verified by conducting two experiments that use data collected from a self-made bearing test rig. The proposed model demonstrates a favorable performance and is more effective and robust than other deep learning methods.