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A Short Survey on Fault Diagnosis of Rotating Machinery Using Entropy Techniques

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Industrial Networks and Intelligent Systems (INISCOM 2017)

Abstract

Fault diagnosis is significant for identifying latent abnormalities, and implementing fault-tolerant operations for minimizing performance degradation caused by failures in industrial systems, such as rotating machinery. The emergence of entropy theory contributes to precisely measure irregularity and complexity in a time series, which can be used for discriminating prominent fault information in rotating machinery. In this short paper, the utilization of entropy techniques for fault diagnosis of rotating machinery is summarized. Finally, open research trends and conclusions are discussed and presented respectively.

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Acknowledgement

This work is partially supported by International and Hong Kong, Macao & Taiwan collaborative innovation platform and major international cooperation projects of colleges in Guangdong Province (No. 2015KGJHZ026), The Natural Science Foundation of Guangdong Province (No. 2016A030307029), and Maoming Engineering Research Center on Industrial Internet of Things (No. 517018).

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Authors and Affiliations

  1. School of Engineering, University of Lincoln, Lincoln, UK

    Zhiqiang Huo, Yu Zhang & Lei Shu

  2. Guangdong Provincial Key Laboratory on Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China

    Zhiqiang Huo & Lei Shu

Authors
  1. Zhiqiang Huo
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  2. Yu Zhang
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  3. Lei Shu
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Corresponding author

Correspondence to Zhiqiang Huo .

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Editors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Yuanfang Chen

  2. Queen’s University Belfast, Belfast, United Kingdom

    Trung Q. Duong

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Huo, Z., Zhang, Y., Shu, L. (2018). A Short Survey on Fault Diagnosis of Rotating Machinery Using Entropy Techniques. In: Chen, Y., Duong, T. (eds) Industrial Networks and Intelligent Systems. INISCOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-74176-5_24

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  • DOI: https://doi.org/10.1007/978-3-319-74176-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74175-8

  • Online ISBN: 978-3-319-74176-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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