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Structural Health Monitoring

An Advanced Signal Processing Perspective

  • Book
  • © 2017

Overview

Editors:
  1. Ruqiang Yan

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

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  2. Xuefeng Chen

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Subhas Chandra Mukhopadhyay

    1. Department of Engineering, Macquarie University, Sydney, Australia

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  • Focuses on newly-developed signal processing techniques and their applications to various mechanical and structural systems
  • Encompasses interdisciplinary areas, such as smart materials, sensors and actuators, damage diagnosis and prognosis, signal and image processing algorithms, and wireless intelligent sensing
  • Written by leading experts in the field
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xi
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  2. Advanced Signal Processing for Structural Health Monitoring

    • Ruqiang Yan, Xuefeng Chen, Subhas C. Mukhopadhyay
    Pages 1-11

  3. Signal Post-processing for Accurate Evaluation of the Natural Frequencies

    • G. R. Gillich, I. C. Mituletu
    Pages 13-37

  4. Holobalancing Method and Its Improvement by Reselection of Balancing Object

    • Yuhe Liao, Liangsheng Qu
    Pages 39-63

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

    • Shuilong He, Yikun Liu, Jinglong Chen, Yanyang Zi
    Pages 65-91

  6. Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration

    • Binqiang Chen, Wangpeng He, Nianyin Zeng
    Pages 93-130

  7. Time-Frequency Manifold for Machinery Fault Diagnosis

    • Qingbo He, Xiaoxi Ding
    Pages 131-154

  8. Matching Demodulation Transform and Its Application in Machine Fault Diagnosis

    • Xuefeng Chen, Shibin Wang
    Pages 155-202

  9. Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery

    • Gang Tang, Huaqing Wang, Yanliang Ke, Ganggang Luo
    Pages 203-225

  10. Sparse Representation of the Transients in Mechanical Signals

    • Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang, Juanjuan Shi
    Pages 227-258

  11. Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition

    • Yaguo Lei
    Pages 259-292

  12. Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring

    • Wenxian Yang
    Pages 293-319

  13. Time-Frequency Demodulation Analysis Based on LMD and Its Applications

    • Yanxue Wang, Xuefeng Chen, Yanyang Zi
    Pages 321-345

  14. On the Use of Stochastic Resonance in Mechanical Fault Signal Detection

    • X. F. Zhang, N. Q. Hu, L. Zhang, X. F. Wu, L. Hu, Z. Cheng
    Pages 347-375
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Keywords

About this book

This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.

Editors and Affiliations

  • School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Ruqiang Yan

  • School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

    Xuefeng Chen

  • Department of Engineering, Macquarie University, Sydney, Australia

    Subhas Chandra Mukhopadhyay

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