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Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2018)

Part of the book series: Applied Condition Monitoring ((ACM,volume 15))

Abstract

Fault diagnosis is vital for the health management of rotating machinery. The non-stationary working conditions is one of the major challenges in this field. The key is to extract working-condition-invariant but fault-discriminative features. Traditional methods use expert knowledge on the machines and signal processing to extract fault features from vibration signals manually. This paper regards this issue as a domain adaption problem and utilizes deep learning technique to learn fault discriminative features automatically. We teach deep Convolutional Neural Networks to pronounce diagnostic results from raw vibration data and propose a Rotating Speed Normalization method to improve the domain adaption ability of the neural network models. A case study of rotor crack diagnosis under non-stationary and ever-changing rotating speeds is presented. Using 95600 signal segments, we compare the diagnostic performance of ours and reported Convolutional Neural Network models. The results show that our model gives solid diagnostic accuracy from non-stationary vibration signals, and the proposed Rotating Speed Normalization method can successfully boost the performance of all investigated CNN models.

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Acknowledgment

This research is supported by National Natural Science Foundation of China (51305067), National Key Research and Development Program of China (2017YF- C0108401, 2016YFB1200401), Natural Sciences and Engineering Research Council of Canada (Grant #RGPIN-2015-04897), and Future Energy Systems research under Canada First Research Excellent Fund (CFREF) (FES-T14-P02). The authors would also like to acknowledge the help of Mr. Peng Chen and Mr. Mian Zhang.

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Authors and Affiliations

  1. Equipment Reliability, Prognostics and Health Management Lab, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Dongdong Wei & KeSheng Wang

  2. Centre for Asset Integrity Management, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, South Africa

    Stephan Heyns

  3. Department of Mechanical Engineering, University of Alberta, Edmonton, Canada

    Ming J. Zuo

Authors
  1. Dongdong Wei
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  2. KeSheng Wang
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  3. Stephan Heyns
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  4. Ming J. Zuo
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Corresponding author

Correspondence to KeSheng Wang .

Editor information

Editors and Affiliations

  1. Department of Structural and Mechanical Engineering, Faculty of Industrial and Telecommunications Engineering, University of Cantabria, Santander, Spain

    Alfonso Fernandez Del Rincon

  2. Department of Structural and Mechanical Engineering, Faculty of Industrial and Telecommunications Engineering, University of Cantabria, Santander, Spain

    Fernando Viadero Rueda

  3. Department of Mechanical Engineering, National School of Engineers of Sfax, Sfax, Tunisia

    Fakher Chaari

  4. Institute of Mining Engineering, Wroclaw University of Technology, Wrocław, Poland

    Radoslaw Zimroz

  5. National School of Engineers of Sfax, Sfax, Tunisia

    Mohamed Haddar

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Wei, D., Wang, K., Heyns, S., Zuo, M.J. (2019). Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-11220-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11219-6

  • Online ISBN: 978-3-030-11220-2

  • eBook Packages: EngineeringEngineering (R0)

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