Experimental Investigation of a New Global Damage Detection Scheme PDF Download

Author: Seksan Ratanasumritkul
Publisher:
ISBN:
Category : Civil engineering
Languages : en
Pages : 138

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Author: Muhammad S. Shohel
Publisher:
ISBN:
Category : Civil engineering
Languages : en
Pages : 226

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Book Description

Author: Dabit Acharya
Publisher:
ISBN:
Category : Civil engineering
Languages : en
Pages : 228

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Book Description
"The main objective of this study is to show the specific capabilities of the Scanning Laser Vibrometer (SLV) for global damage detection using a recent defect energy parameter technique proposed by Saleeb and coworkers. The experimental technique used for extraction of signature is the first and most important part in any damage detection technique. Signatures considered here are full-field SLV measurements for modal shapes and associated frequencies of plated structures. The damage feature extraction capability was studied extensively by analyzing various simulation and experimental results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure{u2019}s response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The signal noise ratio of experiment shows the capability of the same experiment to be used for damage detection purpose. Various experiments were performed to verify a significant signal noise ratio for a successful detection. Very high number of scanning points, for optical experimental measurement, for any civil structure can be impractical and uneconomical. So, a pragmatic direction for the development of new experimental measurement tools was studied where different number of scanning points and different types of statically loaded simulations were performed to verify the specific capabilities of the defect energy parameter technique. It was further observed that powerful graphic user interface should also be an integral part in any present in the damage detection scheme for successful and more accurate detection. Furthermore, some potential use of SLV techniques in detection are provided, both for dynamic and static applications."--abstract.

Author: Andrew Stephen Ward
Publisher:
ISBN: 9781124665467
Category :
Languages : en
Pages :

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Book Description
Damage detection and localization allow for automated real-time monitoring of realworld engineering projects. The benefits of such a system include improved safety, lower maintenance costs, and higher reliability. Many of the early works focus almost exclusively on numerical simulations of real systems, with very little experimentally acquired data used in detection. Introducing real world data complicates the analysis significantly by requiring noise reducing techniques to acquire legitimate results. In addition, the cost of obtaining enough data to fully define a damaged system can quickly become prohibitive. This thesis focuses directly on damage detection schemes carried out through empirical means. First a concept proving scheme is used by which data about the system is collected through accelerometer data. The damage detection scheme requires the reduction of a large set of data to one or two descriptive eigenparameters. Second, the scheme is repeated using optically gathered data through useof a high speed camera and software image manipulation tools. Damage detection is shown to be possible under the some conditions and initial parameters. Localization of the damage, however, is shown to require sensor information from multiple locations. Further still the optically based method is shown to supplement a failed detection by other means.

Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721152452
Category :
Languages : en
Pages : 154

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Book Description
The main objective of this study is to assess the specific capabilities of the defect energy parameter technique for global damage detection developed by Saleeb and coworkers. The feature extraction is the most important capability in any damage-detection technique. Features are any parameters extracted from the processed measurement data in order to enhance damage detection. The damage feature extraction capability was studied extensively by analyzing various simulation results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure's response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The arrangement of fine/extensive sensor network to measure required data for the detection is an "unlimited" ability, but there is a difficulty to place extensive number of sensors on a structure. Therefore, an investigation was conducted using the measurements of coarse sensor network. The white and the pink noises, which cover most of the frequency ranges that are typically encountered in the many measuring devices used (e.g., accelerometers, strain gauges, etc.) are added to the displacements to investigate the effect of noisy measurements in the detection technique. The noisy displacements and the noisy damage parameter values are used to study the signal feature reconstruction using wavelets. The enhancement of the feature extraction capability was successfully achieved by the wavelet theory. Saleeb, Atef F. and Ponnaluru, Gopi Krishna Glenn Research Center NCC3-808; WBS 846-02-07-03

Author:
Publisher:
ISBN:
Category : Nondestructive testing
Languages : en
Pages : 712

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Book Description

Author: Atef F. Saleeb
Publisher: BiblioGov
ISBN: 9781289147136
Category :
Languages : en
Pages : 156

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Book Description
The main objective of this study is to assess the specific capabilities of the defect energy parameter technique for global damage detection developed by Saleeb and coworkers. The feature extraction is the most important capability in any damage-detection technique. Features are any parameters extracted from the processed measurement data in order to enhance damage detection. The damage feature extraction capability was studied extensively by analyzing various simulation results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure's response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The arrangement of fine/extensive sensor network to measure required data for the detection is an "unlimited" ability, but there is a difficulty to place extensive number of sensors on a structure. Therefore, an investigation was conducted using the measurements of coarse sensor network. The white and the pink noises, which cover most of the frequency ranges that are typically encountered in the many measuring devices used (e.g., accelerometers, strain gauges, etc.) are added to the displacements to investigate the effect of noisy measurements in the detection technique. The noisy displacements and the noisy damage parameter values are used to study the signal feature reconstruction using wavelets. The enhancement of the feature extraction capability was successfully achieved by the wavelet theory.

Author: Alfred Wicks
Publisher: Springer Science & Business
ISBN: 3319045709
Category : Technology & Engineering
Languages : en
Pages : 288

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Book Description
This fifth volume of eight from the IMAC - XXXII Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Linear Systems Substructure Modelling Adaptive Structures Experimental Techniques Analytical Methods Damage Detection Damping of Materials & Members Modal Parameter Identification Modal Testing Methods System Identification Active Control Modal Parameter Estimation Processing Modal Data

Author: M. Arif Wani
Publisher: Springer
ISBN: 9789811567582
Category : Technology & Engineering
Languages : en
Pages : 300

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Book Description
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Author: Tshilidzi Marwala
Publisher: John Wiley & Sons
ISBN: 111915300X
Category : Technology & Engineering
Languages : en
Pages : 248

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Book Description
Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.