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Structural Health Monitoring: An Advanced Signal Processing Perspective Softcover reprint of the original 1st ed. 2017 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 375 pages, height x width: 235x155 mm, weight: 5854 g, 175 Illustrations, color; 109 Illustrations, black and white; XI, 375 p. 284 illus., 175 illus. in color., 1 Paperback / softback
  • Sērija : Smart Sensors, Measurement and Instrumentation 26
  • Izdošanas datums: 25-Jul-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319858327
  • ISBN-13: 9783319858326
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  • Cena: 136,16 €*
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Structural Health Monitoring: An Advanced Signal Processing Perspective Softcover reprint of the original 1st ed. 2017Palielināt
  • Formāts: Paperback / softback, 375 pages, height x width: 235x155 mm, weight: 5854 g, 175 Illustrations, color; 109 Illustrations, black and white; XI, 375 p. 284 illus., 175 illus. in color., 1 Paperback / softback
  • Sērija : Smart Sensors, Measurement and Instrumentation 26
  • Izdošanas datums: 25-Jul-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319858327
  • ISBN-13: 9783319858326
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319858326.html
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.
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.