Bearing Fault Detection with Data Augmentation Based on 2-D CNN and 1-D CNN

Abstract

Bearings are one of the most essential parts of rotary machines. The failure of bearings can lead to significant financial loss as well as personal casualties. Therefore, bearing defect diagnosis is a very important research project. Recently, a lot of bearing defect diagnosis studies using deep learning methods have been conducted. However, there are some challenges to be addressed. In a real working condition, there is always much more normal data than fault data, so a data imbalance problem exists. To address this situation, data augmentation method which generates more training data from the original data, was used. This method was done by applying a geometric transformation so that the class label did not be changed. Therefore, in this paper, we compared the results of using and without data augmentation technique through 1-D CNN and 2-D CNN deep learning algorithm that are effective on time series data analysis and pattern recognition. Finally, we obtained better results when using data augmentation technique.