Learning convolution neural network with shift pitching based data augmentation for vibration analysis

Abstract

Data augmentation is a common approach that been implemented in order to increase the training data quantity for Convolutional Neural Networks in signal processing, image recognition and speech recognition. However, the conventional data augmentation methods usually implement the window slicing and overlap window slicing methods in the bearing fault analysis. Meanwhile, the audio deformation approach such as time stretching and pitch shifting methods have been commonly used as data augmentation approach in speech recognition. Thus, this paper proposed a data augmentation based on shift pitching technique for the vibration signal. The relationship between the audio and the vibration signal is evaluated for a bearing fault analysis using Convolution Neural Networks. The new dataset produce by the data augmentation is used to increase the number of training dataset and to improve the Convolutional Neural Networks training performance. The result shows that the shift pitching based data augmentation method able to achieve higher training accuracy compared to the window sliding data augmentation. The combinations of all ratio pitch obtain 93% accuracy whilst the accuracy for a single rate pitch are between 81% to 91%.Thus, the proposed method is competent and able to improve the performance of bearing fault classification.