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The Resource Structural Health Monitoring and Damage Detection, Volume 7 : Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017

Structural Health Monitoring and Damage Detection, Volume 7 : Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017

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Structural Health Monitoring and Damage Detection, Volume 7 : Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017
Title
Structural Health Monitoring and Damage Detection, Volume 7
Title remainder
Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017
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Subject
Language
eng
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Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Conference Proceedings of the Society for Experimental Mechanics Ser
Structural Health Monitoring and Damage Detection, Volume 7 : Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017
Label
Structural Health Monitoring and Damage Detection, Volume 7 : Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- 1 Exploiting Spatial Sparsity in Vibration-Based Damage Detection -- 1.1 Introduction -- 1.2 Method of Approach -- 1.3 LASSO Regularization -- 1.4 Simulations and Verification -- 1.5 Effects of Measurement Noise -- 1.6 Conclusion -- References -- 2 Multi-Source Sensing and Analysis for Machine-Array Condition Monitoring -- 2.1 Introduction -- 2.2 Background -- 2.3 Experimental Setup and Procedures -- 2.4 Analysis Approaches -- 2.5 Blind Source Separation -- 2.6 Linear Least Squares -- 2.7 Brute-Force Optimization -- 2.8 Machine Learning Approaches -- 2.9 Evaluation -- 2.10 Further Research -- 2.11 Conclusion -- References -- 3 Wavelet Transform-Based Damage Detection in Reinforced Concrete Using an Air-Coupled Impact-Echo Method -- References -- 4 Damage Detection Based on Strain Transmissibility for Beam Structure by Using Distributed Fiber Optics -- 4.1 Introduction -- 4.2 Transmissibility Functions Algorithm -- 4.3 Strain Transmissibility Function -- 4.4 Simulation Validation -- 4.4.1 Simulation Model -- 4.4.2 Simulation Results -- 4.5 Experiment Validation -- 4.5.1 Brief Introduction of ODiSI-B -- 4.5.2 Experimental Setup -- 4.5.3 Experimental Result -- 4.6 Conclusion -- 4.7 Funding -- References -- 5 Modal Parameters Estimation of an Offshore Wind Turbine Using Measured Acceleration Signals from the Drive Train -- 5.1 Introduction -- 5.2 Data Acquisition -- 5.3 Operational Modal Analysis and Modal Parameters Tracking Approach -- 5.4 Results and Discussions -- 5.4.1 Low Frequency-Band Analysis -- 5.4.2 High Frequency-Band Analysis -- 5.5 Conclusions -- References -- 6 Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications -- 6.1 Introduction -- 6.2 1D and 2D CNNs -- 6.3 The Proposed CNN-Based Algorithm -- 6.4 Experimental Demonstration -- 6.5 Discussions
  • 6.6 Conclusions -- References -- 7 Monitoring the Health of a Cantilever Beam Using Nonlinear Modal Tracking -- Nomenclature -- 7.1 Introduction -- 7.2 Background -- 7.3 Theoretical Model Development -- 7.4 System Identification -- 7.5 Experimental Procedure -- 7.6 Results -- 7.7 Discussion -- 7.8 Conclusion -- References -- 8 Using Modal Parameters for Structural Health Monitoring -- 8.1 Introduction -- 8.2 Review of Modal Assurance Criterion (MAC) -- 8.3 Review of Shape Difference Indicator (SDI) -- 8.4 Identifying Cap Screw Torque -- 8.5 SDI and MAC with Modal Frequency Shapes -- 8.6 Increased SDI Sensitivity -- 8.7 Modal Frequency Shapes with Increased Sensitivity -- 8.8 Modal Damping Shapes with Increased Sensitivity -- 8.9 Fault Correlation Tools (FaCTs{u2122}) -- 8.10 Conclusion -- References -- 9 Current Challenges with BIGDATA Analytics in Structural Health Monitoring -- 9.1 Introduction -- 9.2 Bigdata Characteristics -- 9.2.1 Variety -- 9.2.2 Volume -- 9.2.3 Velocity -- 9.2.4 Complexity -- 9.3 Bigdata Processing -- 9.4 Promises -- 9.5 Conclusion -- References -- 10 Detection of Cracks in Beams Using Treed Gaussian Processes -- 10.1 Introduction -- 10.2 The Previous Approach -- 10.2.1 Gaussian Processes -- 10.2.2 Previous Method: Crack Detection -- 10.3 Treed Gaussian Processes -- 10.3.1 Regression Trees -- 10.4 The Current Data and Results -- 10.4.1 Current Data -- 10.4.2 Results -- 10.5 Conclusions -- References
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Extent
1 online resource (99 pages)
Form of item
online
Isbn
9783319541099
Media category
computer
Media MARC source
rdamedia
Media type code
c
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