Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (1): 233-246.doi: 10.23919/JSEE.2022.000023

• RELIABILITY • Previous Articles    

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Kwame Bensah KULEVOME Delanyo1,2, Hong WANG1,2,*(), Xuegang WANG1()   

  1. 1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    2 Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
  • Received:2020-11-10 Accepted:2021-11-25 Online:2022-01-18 Published:2022-02-22
  • Contact: Hong WANG;
  • About author:|KULEVOME Delanyo Kwame Bensah was born in 1983. He received his B.Eng. degree from Accra Institute of Technology and M.Eng. degree in electronic science and technology in 2019 from University of Electronic Science and Technology of China, Chengdu, China, where he is currently pursuing his Ph.D. degree in information and communication engineering. His research interests include prognostics and health management of systems, signal processing, and deep learning. E-mail:||WANG Hong was born in 1974. He received his B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University, Chongqing University, and University of Electronic Science and Technology of China (UESTC), respectively. He has been a faculty member with UESTC since 2003. From 2007 to 2009 he was engaged in doctoral research with the Second Research Institute of Civil Aviation Administration. From 2009 to 2010 he was a research scholar with Polytechnic Institute of New York University and a research assistant with New Jersey Institute of Technology, USA. His present areas of interest include radar signal processing, avionics, aeronautical telecommunication, and surveillance technologies in ATC. E-mail:||WANG Xuegang was born in 1962. He received his Ph.D. degree from Xidian University in 1992. He is now a professor and Ph.D. supervisor with University of Electronic Science and Technology of China. His research interests include radar signal processing, and millimeter wave radar. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (42027805), National Aeronautical Fund (ASFC-2017 2080005) and National Key R&D Program of China (2017YFC03 07100)


Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.

Key words: bearing failure, deep neural network, fault classification, health indicator, prognostics and health management