Rolling bearing fault identification using multilayer deep learning convolutional neural network

• Main Authors: Hongkai Jiang, Fuan Wang, Haidong Shao, Haizhou Zhang
• Format: Article
• Language: English
• Published: JVE International 2017-02-01
• Series: Journal of Vibroengineering
• Subjects: multilayer deep learning CNN; normalization preprocessing; rolling bearing; fault identification; feature learning
• Online Access: https://www.jvejournals.com/article/16939
• ISSN: 1392-8716; 2538-8460

The vibration signal of rolling bearing is usually complex and the useful fault information is hidden in the background noise, therefore, it is a challenge to identify rolling bearing faults from the complex vibration environment. In this paper, a novel multilayer deep learning convolutional neural network (CNN) method to identify rolling bearing fault is proposed. Firstly, in order to avoid the influence of different characteristics of the input data on the identification accuracy, a normalization preprocessing method is applied to preprocess the vibration signals of rolling bearings. Secondly, a multilayer CNN based on deep learning is designed in this paper to improve the fault identification accuracy of rolling bearing. Simulation data and experimental data analysis results show that the proposed method has better performance than SVM method and ANN method without any manual feature extractor design.