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020 _a9783319561264
_9978-3-319-56126-4
024 7 _a10.1007/978-3-319-56126-4
_2doi
050 4 _aTK5102.9
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_2bicssc
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_2thema
072 7 _aUYS
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082 0 4 _a621.382
_223
245 1 0 _aStructural Health Monitoring
_h[electronic resource] :
_bAn Advanced Signal Processing Perspective /
_cedited by Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXI, 375 p. 284 illus., 175 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSmart Sensors, Measurement and Instrumentation,
_x2194-8410 ;
_v26
505 0 _aAdvanced 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.
520 _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 0 _aSignal processing.
_94052
650 0 _aBiomedical engineering.
_93292
650 0 _aIndustrial engineering.
_931641
650 0 _aProduction engineering.
_93683
650 0 _aMeasurement.
_928731
650 0 _aMeasuring instruments.
_910420
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aIndustrial and Production Engineering.
_931644
650 2 4 _aMeasurement Science and Instrumentation.
_932783
700 1 _aYan, Ruqiang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_959796
700 1 _aChen, Xuefeng.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_959797
700 1 _aMukhopadhyay, Subhas Chandra.
_eeditor.
_0(orcid)0000-0002-8600-5907
_1https://orcid.org/0000-0002-8600-5907
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_959798
710 2 _aSpringerLink (Online service)
_959799
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319561257
776 0 8 _iPrinted edition:
_z9783319561271
776 0 8 _iPrinted edition:
_z9783319858326
830 0 _aSmart Sensors, Measurement and Instrumentation,
_x2194-8410 ;
_v26
_959800
856 4 0 _uhttps://doi.org/10.1007/978-3-319-56126-4
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