Structural Health Monitoring A Non-Deterministic Framework

This book discusses systems of damage detection and structural health monitoring in mechanical, civil, and aerospace structures. It utilizes principles of fuzzy logic, probability theory, and signal processing to develop systems and approaches that are robust in the presence of both noise in the dat...

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Bibliographic Details
Main Author: Ganguli, Ranjan
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2020, 2020
Edition:1st ed. 2020
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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245 0 0 |a Structural Health Monitoring  |h Elektronische Ressource  |b A Non-Deterministic Framework  |c by Ranjan Ganguli 
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300 |a XI, 214 p. 98 illus., 31 illus. in color  |b online resource 
505 0 |a Introduction -- Damage detection using fuzzy logic and probabilistic simulation -- Damage detection using modal curvature and fuzzy logic -- Damage Detection in Composite plates -- Damage detection in smart composite plates -- Damage growth monitoring in composite plates -- Wavelet based damage detection -- Fractal dimension based damage detection. 
653 |a Mechanics, Applied 
653 |a Buildings—Maintenance 
653 |a Computer-Aided Engineering (CAD, CAE) and Design 
653 |a Computer simulation 
653 |a Computer Modelling 
653 |a Buildings—Repair and reconstruction 
653 |a Multibody Systems and Mechanical Vibrations 
653 |a Computer-aided engineering 
653 |a Vibration 
653 |a Building Repair and Maintenance 
653 |a Multibody systems 
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520 |a This book discusses systems of damage detection and structural health monitoring in mechanical, civil, and aerospace structures. It utilizes principles of fuzzy logic, probability theory, and signal processing to develop systems and approaches that are robust in the presence of both noise in the data and variations in properties of materials which are intrinsic to the process of mass production. This volume will be useful to graduate students, researchers, and engineers working in this area, especially those looking to understand and address model uncertainty in their algorithms.