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Analysis of Power Spectrum Feature Based on Slurry Noise in Electromagnetic Flowmeter

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Book cover Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 763))

Abstract

As an essential part of measuring technology, flowmeters have been widely used in industrial production. Accurate flow metering will not only improve the quality of products, promoting economic efficiency and management level, but lays foundation for the assessment of energy saving and environmental sewage discharging. Electromagnetic flowmeter is suitable for slurry flow measurement since it has high reliability, strong corrosion resistance, high measurement precision, and no stopped medium components in the measuring pipe. In order to improve the measurement of slurry noise problem, this paper will analyze the power spectrum of the slurry noise, and then find the relationship between slurry noise and excitation frequency as to provide theoretical basis for frequency switch of the variable frequency electromagnetic flowmeter.

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Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Jie Chen, Qiong Fei, Bin Li & Xiaojie Zheng

Authors
  1. Jie Chen
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  2. Qiong Fei
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  3. Bin Li
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  4. Xiaojie Zheng
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Corresponding author

Correspondence to Jie Chen .

Editor information

Editors and Affiliations

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

    Kang Li

  2. Nanjing Automation Research Institute, Nanjing, China

    Yusheng Xue

  3. Harbin Institute of Technology, Harbin, China

    Shumei Cui

  4. Shanghai University, Shanghai, China

    Qun Niu

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

    Zhile Yang

  6. Cranfield University, Bedford, United Kingdom

    Patrick Luk

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© 2017 Springer Nature Singapore Pte Ltd.

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Chen, J., Fei, Q., Li, B., Zheng, X. (2017). Analysis of Power Spectrum Feature Based on Slurry Noise in Electromagnetic Flowmeter. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_76

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  • DOI: https://doi.org/10.1007/978-981-10-6364-0_76

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6363-3

  • Online ISBN: 978-981-10-6364-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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