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008			170502s2017 sz a o 000 0 eng d
020			_a331956126X _q(electronic bk.)
020			_a9783319561264 _q(electronic bk.)
020			_z3319561251
020			_z9783319561257 _q(print)
040			_aN$T _cN$T _dGW5XE _dEBLCP _dYDX _dN$T _dUAB _dESU _dAZU _dUPM _dIOG _dOCLCF _dCOO _dOTZ _dVT2 _dU3W _dES-MaUEC _bspa
050		4	_aTA656.6 _bS778 2017 EB
245	0	0	_aStructural health monitoring : _ban advanced signal processing perspective _cRuqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay, editors.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xi, 375 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSmart sensors, measurement and instrumentation _x2194-8402 _vvolume 26
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; 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		_a4.3 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		_a4.3 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
505	8		_a5 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		_a6 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
520	3		_aThis 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.
650		7	_aIndicadores de salud _2embne _0(OCoLC)fst01748414 _0 _9147744
700	1		_aChen, Xuefeng, _eeditor literario
700	1		_aMukhopadhyay, Subhas Chandra, _eeditor literario _997236
700	1		_aYan, Ruqiang, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-56126-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95915 _d95915 _x1