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Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images

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journal contribution
posted on 29.03.2021, 08:05 by Haidong Shao, Min Xia, Guangjie Han, Eve Zhang, Jiafu Wan
The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

Funding

National Key Research and Development Program under Grant 2018YFB1700500

National Science and Technology Major Project under Grant 2017-V-0011-0062

National Natural Science Foundation of China under Grant 51905160

Natural Science Foundation of Hunan Province under Grant 2020JJ5072

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

5

Pages

3488 - 3496

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

23/06/2020

Publication date

2020-06-30

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Dr Eve Zhang. Deposit date: 22 March 2021

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