000781149 000__ 04855cam\a2200505Ii\4500 000781149 001__ 781149 000781149 005__ 20230306143221.0 000781149 006__ m\\\\\o\\d\\\\\\\\ 000781149 007__ cr\nn\nnnunnun 000781149 008__ 170502s2017\\\\sz\a\\\\o\\\\\000\0\eng\d 000781149 019__ $$a985651404$$a985760415$$a986157447$$a986450780$$a986561523$$a986808876 000781149 020__ $$a9783319561264$$q(electronic book) 000781149 020__ $$a331956126X$$q(electronic book) 000781149 020__ $$z9783319561257 000781149 020__ $$z3319561251 000781149 035__ $$aSP(OCoLC)ocn985105725 000781149 035__ $$aSP(OCoLC)985105725$$z(OCoLC)985651404$$z(OCoLC)985760415$$z(OCoLC)986157447$$z(OCoLC)986450780$$z(OCoLC)986561523$$z(OCoLC)986808876 000781149 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dEBLCP$$dYDX$$dN$T$$dUAB 000781149 049__ $$aISEA 000781149 050_4 $$aTA656.6 000781149 08204 $$a624.1/7$$223 000781149 24500 $$aStructural health monitoring :$$ban advanced signal processing perspective /$$cRuqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay, editors. 000781149 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017. 000781149 300__ $$a1 online resource (xi, 375 pages) :$$billustrations. 000781149 336__ $$atext$$btxt$$2rdacontent 000781149 337__ $$acomputer$$bc$$2rdamedia 000781149 338__ $$aonline resource$$bcr$$2rdacarrier 000781149 4901_ $$aSmart sensors, measurement and instrumentation,$$x2194-8402 ;$$vvolume 26 000781149 5050_ $$aPreface; Contents; About the Editors; 1 Advanced Signal Processing for Structural Health Monitoring; Abstract; 1 Introduction; 2 Structural Health Monitoring; 2.1 Operational Evaluation; 2.2 Data Acquisition; 2.3 Feature Extraction; 2.4 Diagnosis and Prognosis; 3 Signal Processing in SHM; References; 2 Signal Post-processing for Accurate Evaluation of the Natural Frequencies; Abstract; 1 Introduction; 2 Motivation; 3 Standard Frequency Evaluation; 4 Simple Methods to Improve the Frequency Readability; 5 Description and Implementation of the Iterative Algorithm 000781149 5058_ $$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 000781149 5058_ $$a4.3 Balancing Object Selection: Characteristic Analysis of IPV+ and IPV− [7]5 Experimental Verification and Case Study; 5.1 Experimental Verification; 5.2 Case Study; 6 Conclusion and Discussion; References; 4 Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery; Abstract; 1 Introduction; 2 Wavelet Transform Based on Inner Product; 2.1 Inner Product; 2.2 CWT, DWT and WPT; 2.3 Inner Product Validation of WT in RMFD; 3 Adaptive Multiwavelet for RMFD; 3.1 Summary of Multiwavelet Theory; 3.2 Adaptive Multiwavelet Construction; 3.3 Experimental Study; 4 Discussion 000781149 5058_ $$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 000781149 5058_ $$a4.3 Spatial-Spectral Ensemble Kurtosis and Its Kurtogram4.4 Numerical Simulations and Engineering Applications; 5 Adaptive Super-Wavelet Based Kurtogram; 5.1 Adaptive Super-Wavelet Transform; 5.2 A Sparse Indictor: Fault Feature Ratio (FFR); 5.3 Adaptive ESW Based Kurtogram; 5.4 Engineering Applications; 6 Conclusions; Acknowledgements; References; 6 Time-Frequency Manifold for Machinery Fault Diagnosis; Abstract; 1 Introduction; 2 Time-Frequency Manifold Analysis; 2.1 Principle; 2.2 Phase Space Reconstruction; 2.3 Time-Frequency Distribution; 2.4 TFM Learning 000781149 506__ $$aAccess limited to authorized users. 000781149 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 5, 2017). 000781149 650_0 $$aStructural health monitoring. 000781149 7001_ $$aYan, Ruqiang,$$eeditor. 000781149 7001_ $$aChen, Xuefeng,$$eeditor. 000781149 7001_ $$aMukhopadhyay, Subhas Chandra,$$eeditor. 000781149 77608 $$iPrint version:$$z9783319561257$$z3319561251$$w(OCoLC)975368083 000781149 830_0 $$aSmart sensors, measurement and instrumentation ;$$v26. 000781149 852__ $$bebk 000781149 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-56126-4$$zOnline Access$$91397441.1 000781149 909CO $$ooai:library.usi.edu:781149$$pGLOBAL_SET 000781149 980__ $$aEBOOK 000781149 980__ $$aBIB 000781149 982__ $$aEbook 000781149 983__ $$aOnline 000781149 994__ $$a92$$bISE