Structural Health Monitoring An Advanced Signal Processing Perspective

Structural Health Monitoring An Advanced Signal Processing Perspective / edited by Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay.

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 t...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Yan, Ruqiang (Editor), Chen, Xuefeng (Editor), Mukhopadhyay, Subhas Chandra (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Smart Sensors, Measurement and Instrumentation, 26
Springer eBook Collection.
Subjects:
Signal processing.
Image processing.
Speech processing systems.
Biomedical engineering.
Industrial engineering.
Production engineering.
Physical measurements.
Measurement   .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Advanced Signal Processing for Structural Health Monitoring
  • Signal Post-Processing for Accurate Evaluation of the Natural Frequencies
  • Holobalancing Method and its Improvement by Reselection of Balancing Object
  • Wavelet Transform Based On Inner Product for Fault Diagnosis of Rotating Machinery
  • Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration
  • Time-Frequency Manifold for Machinery Fault Diagnosis
  • Matching Demodulation Transform and its Application in Machine Fault Diagnosis
  • Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery
  • Sparse Representation of the Transients in Mechanical Signals
  • Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition
  • Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring
  • Time-Frequency Demodulation Analysis Based on LMD and Its Applications
  • On The Use of Stochastic Resonance in Mechanical Fault Signal Detection.

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