© 2017

Structural Health Monitoring

An Advanced Signal Processing Perspective

  • Ruqiang Yan
  • Xuefeng Chen
  • Subhas Chandra Mukhopadhyay

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

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ruqiang Yan, Xuefeng Chen, Subhas C. Mukhopadhyay
    Pages 1-11
  3. Shuilong He, Yikun Liu, Jinglong Chen, Yanyang Zi
    Pages 65-91
  4. Qingbo He, Xiaoxi Ding
    Pages 131-154
  5. Gang Tang, Huaqing Wang, Yanliang Ke, Ganggang Luo
    Pages 203-225
  6. Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang, Juanjuan Shi
    Pages 227-258
  7. Yanxue Wang, Xuefeng Chen, Yanyang Zi
    Pages 321-345
  8. X. F. Zhang, N. Q. Hu, L. Zhang, X. F. Wu, L. Hu, Z. Cheng
    Pages 347-375

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.


Structural Health Monitoring Signal Processing Condition Monitoring Fault Diagnostics Wavelet Theory Time-frequency Analysis Empirical Mode Decomposition Sparse Representation Wind Turbine Gas Turbine Machine Tools

Editors and affiliations

  • Ruqiang Yan
    • 1
  • Xuefeng Chen
    • 2
  • Subhas Chandra Mukhopadhyay
    • 3
  1. 1.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.Department of EngineeringMacquarie UniversitySydneyAustralia

Bibliographic information

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