Structural Health Monitoring
An Advanced Signal Processing Perspective
Published on 25. July 2018
Book
Paperback/Softback
XI, 375 pages
978-3-319-85832-6 (ISBN)
Description
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
109 s/w Abbildungen, 175 farbige Abbildungen
XI, 375 p. 284 illus., 175 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 21 mm
Weight
587 gr
ISBN-13
978-3-319-85832-6 (9783319858326)
DOI
10.1007/978-3-319-56126-4
Schweitzer Classification
Other editions
Additional editions
Ruqiang Yan | Xuefeng Chen | Subhas Chandra Mukhopadhyay
Structural Health Monitoring
An Advanced Signal Processing Perspective
Book
05/2017
Springer
€160.49
Shipment within 10-15 days
Content
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.