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Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery

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Structural Health Monitoring

Part of the book series: Smart Sensors, Measurement and Instrumentation ((SSMI,volume 26))

Abstract

As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation experiments. Then the newer development of WT based on inner product is introduced. The construction and applications of major developments on adaptive multiwavelet in RMFD are presented. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed.

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Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Shuilong He, Yikun Liu, Jinglong Chen & Yanyang Zi

Authors
  1. Shuilong He
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  2. Yikun Liu
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  3. Jinglong Chen
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  4. Yanyang Zi
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Correspondence to Shuilong He .

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Editors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Ruqiang Yan

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

    Xuefeng Chen

  3. Department of Engineering, Macquarie University, Sydney, New South Wales, Australia

    Subhas Chandra Mukhopadhyay

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He, S., Liu, Y., Chen, J., Zi, Y. (2017). Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery. In: Yan, R., Chen, X., Mukhopadhyay, S. (eds) Structural Health Monitoring. Smart Sensors, Measurement and Instrumentation, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-56126-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-56126-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56125-7

  • Online ISBN: 978-3-319-56126-4

  • eBook Packages: EngineeringEngineering (R0)

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