Structural health monitoring : an advanced signal processing perspective

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
Other Authors: Yan, Ruqiang, (Editor), Chen, Xuefeng, (Editor), Mukhopadhyay, Subhas Chandra, (Editor)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Series: Smart sensors, measurement and instrumentation ; 26.
Subjects:
Structural health monitoring.
elektronické knihy
electronic books
ISBN: 9783319561264
9783319561257
Physical Description: 1 online resource (xi, 375 pages) : illustrations (some color)

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245 0 0 |a Structural health monitoring :  |b an advanced signal processing perspective /  |c Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay, editors. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (xi, 375 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Smart sensors, measurement and instrumentation,  |x 2194-8402 ;  |v volume 26 
505 0 |a Preface; Contents; About the Editors; 1 Advanced Signal Processing for Structural Health Monitoring; Abstract; 1 Introduction; 2 Structural Health Monitoring; 2.1 Operational Evaluation; 2.2 Data Acquisition; 2.3 Feature Extraction; 2.4 Diagnosis and Prognosis; 3 Signal Processing in SHM; References; 2 Signal Post-processing for Accurate Evaluation of the Natural Frequencies; Abstract; 1 Introduction; 2 Motivation; 3 Standard Frequency Evaluation; 4 Simple Methods to Improve the Frequency Readability; 5 Description and Implementation of the Iterative Algorithm. 
505 8 |a 6 Testing the Algorithm Efficiency7 Conclusions; Acknowledgements; References; 3 Holobalancing Method and Its Improvement by Reselection of Balancing Object; Abstract; 1 Introduction; 2 Construction of Holospectrum; 2.1 Basic Condition Required; 2.2 Three-Dimensional Holospectrum (3dH); 3 Introduction of Holobalancing Method; 3.1 Initial Phase Point (IPP); 3.2 Precession Angle Compensation; 3.3 Differential Holospectrum and Transfer Matrix; 3.4 The Balancing Procedure; 4 Balancing Object Reselection; 4.1 Characteristic and Deficiency of the IPV; 4.2 Precession Decomposition. 
505 8 |a 4.3 Balancing Object Selection: Characteristic Analysis of IPV+ and IPV− [7]5 Experimental Verification and Case Study; 5.1 Experimental Verification; 5.2 Case Study; 6 Conclusion and Discussion; References; 4 Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery; Abstract; 1 Introduction; 2 Wavelet Transform Based on Inner Product; 2.1 Inner Product; 2.2 CWT, DWT and WPT; 2.3 Inner Product Validation of WT in RMFD; 3 Adaptive Multiwavelet for RMFD; 3.1 Summary of Multiwavelet Theory; 3.2 Adaptive Multiwavelet Construction; 3.3 Experimental Study; 4 Discussion 
505 8 |a 5 ConclusionReferences; 5 Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration; Abstract; 1 A Brief Introduction; 2 Spectral Kurtosis and Fast Kurtogram; 2.1 Signal Modelling; 2.2 Spectral Kurtosis; 2.3 Illustration Example of Spectral Kurtosis; 3 Wavelet Based Kurtogram and Its Development; 3.1 STFT Based Kurtogram; 3.2 Fast Kurtogram; 3.3 Wavelet Packet Based Kurtogram; 4 Wavelet Tight Frame Based Kurtogram; 4.1 Limitation of Original Kurtogram; 4.2 Quasi-Analytic Wavelet Tight Frame. 
505 8 |a 4.3 Spatial-Spectral Ensemble Kurtosis and Its Kurtogram4.4 Numerical Simulations and Engineering Applications; 5 Adaptive Super-Wavelet Based Kurtogram; 5.1 Adaptive Super-Wavelet Transform; 5.2 A Sparse Indictor: Fault Feature Ratio (FFR); 5.3 Adaptive ESW Based Kurtogram; 5.4 Engineering Applications; 6 Conclusions; Acknowledgements; References; 6 Time-Frequency Manifold for Machinery Fault Diagnosis; Abstract; 1 Introduction; 2 Time-Frequency Manifold Analysis; 2.1 Principle; 2.2 Phase Space Reconstruction; 2.3 Time-Frequency Distribution; 2.4 TFM Learning. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
504 |a Includes bibliographical references at the end of each chapters. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Structural health monitoring. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Yan, Ruqiang,  |e editor. 
700 1 |a Chen, Xuefeng,  |e editor. 
700 1 |a Mukhopadhyay, Subhas Chandra,  |e editor. 
776 0 8 |i Print version:  |t Structural health monitoring.  |d Cham, Switzerland : Springer, 2017  |z 9783319561257  |z 3319561251  |w (OCoLC)975368083 
830 0 |a Smart sensors, measurement and instrumentation ;  |v 26. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-56126-4  |y Plný text 
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