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Structural Health Monitoring : A Non-Deterministic Framework / Ranjan Ganguli

By: Ganguli, Ranjan, autor
Contributor(s): SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Engineering (Springer-11647)).Publisher: Singapore : Springer Singapore : Imprint: Springer, 2020Edition: First edition.Description: 1 recurso en línea (XI, 214 páginas) : 98 ilustraciones, 31 ilustraciones a color.ISBN: 9789811549885.Subject: Estructuras (Construcción) -- MonitorizaciónOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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. .
Abstract: 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. .
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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. .

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. .

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