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Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit

  • Ke ZhaoEmail author
  • Haidong Shao
Article
  • 101 Downloads

Abstract

Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods.

Keywords

Rolling bearing fault diagnosis Deep gated recurrent unit Artificial fish swarm algorithm Extreme learning machine Feature learning ability 

Notes

Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 51475368) and the Aviation Science Foundation of China (No. 20170253003).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of AeronauticsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina

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