Structural Health Monitoring

An Advanced Signal Processing Perspective
Softcover Nachdruck of the original 1. Auflage 2017
ISBN: 978-3-319-85832-6
Verlag: Springer International Publishing

Buch, Englisch, Band 26, 375 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 5854 g

Reihe: Smart Sensors, Measurement and Instrumentation

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.

Zielgruppe

Research

Autoren/Hrsg.

Yan, Ruqiang

Mukhopadhyay, Subhas Chandra

Chen, Xuefeng

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Zerstörungsfreie Werkstoffprüfung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik

Inhaltsverzeichnis

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.