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Structural health monitoringan advan...

Chen, Xuefeng.

Structural health monitoringan advanced signal processing perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structural health monitoringedited by Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay.
其他題名:	an advanced signal processing perspective /
其他作者:	Yan, Ruqiang.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xi, 375 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Structural health monitoring.
電子資源:	http://dx.doi.org/10.1007/978-3-319-56126-4
ISBN:	9783319561264$q(electronic bk.)

Structural health monitoringan advanced signal processing perspective /
Structural health monitoringan advanced signal processing perspective /[electronic resource] :edited by Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay. - Cham :Springer International Publishing :2017. - xi, 375 p. :ill. (some col.), digital ;24 cm. - Smart sensors, measurement and instrumentation,v.262194-8402 ;. - Smart sensors, measurement and instrumentation ;v.8..

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.

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.

ISBN: 9783319561264$q(electronic bk.)

Standard No.: 10.1007/978-3-319-56126-4doiSubjects--Topical Terms:

522561
Structural health monitoring.

LC Class. No.: TA656.6

Dewey Class. No.: 624.17

Structural health monitoringan advanced signal processing perspective /
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505 0 $a 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.
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.
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