The Experts below are selected from a list of 172269 Experts worldwide ranked by ideXlab platform
Filippo Ubertini - One of the best experts on this subject based on the ideXlab platform.
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Experimental wind tunnel study of a smart sensing skin for Condition Evaluation of a wind turbine blade
Smart Materials and Structures, 2017Co-Authors: Austin Downey, Simon Laflamme, Filippo UbertiniAbstract:© 2017 IOP Publishing Ltd. Condition Evaluation of wind turbine blades is difficult due to their large size, complex geometry and lack of economic and scalable sensing technologies capable of detecting, localizing, and quantifying faults over a blade's global area. A solution is to deploy inexpensive large area electronics over strategic areas of the monitored component, analogous to sensing skin. The authors have previously proposed a large area electronic consisting of a soft elastomeric capacitor (SEC). The SEC is highly scalable due to its low cost and ease of fabrication, and can, therefore, be used for monitoring large-scale components. A single SEC is a strain sensor that measures the additive strain over a surface. Recently, its application in a hybrid dense sensor network (HDSN) configuration has been studied, where a network of SECs is augmented with a few off-the-shelf strain gauges to measure boundary Conditions and decompose the additive strain to obtain unidirectional surface strain maps. These maps can be analyzed to detect, localize, and quantify faults. In this work, we study the performance of the proposed sensing skin at conducting Condition Evaluation of a wind turbine blade model in an operational environment. Damage in the form of changing boundary Conditions and cuts in the monitored substrate are induced into the blade. An HDSN is deployed onto the interior surface of the substrate, and the blade excited in a wind tunnel. Results demonstrate the capability of the HDSN and associated algorithms to detect, localize, and quantify damage. These results show promise for the future deployment of fully integrated sensing skins deployed inside wind turbine blades for Condition Evaluation.
Austin Downey - One of the best experts on this subject based on the ideXlab platform.
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Experimental wind tunnel study of a smart sensing skin for Condition Evaluation of a wind turbine blade
Smart Materials and Structures, 2017Co-Authors: Austin Downey, Simon Laflamme, Filippo UbertiniAbstract:© 2017 IOP Publishing Ltd. Condition Evaluation of wind turbine blades is difficult due to their large size, complex geometry and lack of economic and scalable sensing technologies capable of detecting, localizing, and quantifying faults over a blade's global area. A solution is to deploy inexpensive large area electronics over strategic areas of the monitored component, analogous to sensing skin. The authors have previously proposed a large area electronic consisting of a soft elastomeric capacitor (SEC). The SEC is highly scalable due to its low cost and ease of fabrication, and can, therefore, be used for monitoring large-scale components. A single SEC is a strain sensor that measures the additive strain over a surface. Recently, its application in a hybrid dense sensor network (HDSN) configuration has been studied, where a network of SECs is augmented with a few off-the-shelf strain gauges to measure boundary Conditions and decompose the additive strain to obtain unidirectional surface strain maps. These maps can be analyzed to detect, localize, and quantify faults. In this work, we study the performance of the proposed sensing skin at conducting Condition Evaluation of a wind turbine blade model in an operational environment. Damage in the form of changing boundary Conditions and cuts in the monitored substrate are induced into the blade. An HDSN is deployed onto the interior surface of the substrate, and the blade excited in a wind tunnel. Results demonstrate the capability of the HDSN and associated algorithms to detect, localize, and quantify damage. These results show promise for the future deployment of fully integrated sensing skins deployed inside wind turbine blades for Condition Evaluation.
K Ramanjaneyulu - One of the best experts on this subject based on the ideXlab platform.
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Condition Evaluation of existing reinforced concrete bridges using fuzzy based analytic hierarchy approach
Expert Systems With Applications, 2008Co-Authors: Saptarshi Sasmal, K RamanjaneyuluAbstract:There is an urgent need for a systematic methodology for Condition assessment of the bridges because the old bridges of most of the countries are inadequate to carry current-day traffic and satisfy the present codal provisions and even in newer bridges, deterioration caused by unforeseen service Condition and deferred maintenance is of great concern. In view of this, an attempt has been made in this study to develop a systematic procedure and formulations for Condition Evaluation of existing bridges using Analytic Hierarchy Process in a fuzzy environment. Analytic Hierarchy Process (AHP) is an efficient decision making tool for complicated problems with multiple Evaluation criteria and uncertainty. Fuzzy logic approach has been used to take care of the uncertainties and imprecision in the bridge inspector's observations. In this paper, first, a methodology has been proposed for Condition ranking of number of reinforced concrete (RC) bridges. Then, based on the results obtained from prioritization, rating of the most deserved bridge has been carried out using MADM based fuzzy logic. Computer programs have been developed based on the formulations presented in this paper for evaluating Condition of existing bridges and the details are presented in the paper. The methodology and its application are demonstrated through a case study. This methodology would certainly help the engineers and policy makers concerned with bridge management to overcome the problem related to prioritization and decision on funding related to rehabilitation of bridges.
Daniel L. Magnus - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture classification for rail surface Condition Evaluation
2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016Co-Authors: Tomas F. Yago Vicente, Dimitris Samaras, Michael Petrucci, Daniel L. MagnusAbstract:Rail surface defects threaten train and passenger safety. Hence rail surfaces must be restored using different processes depending on measurement of the severity of the defects. In this paper, we propose a new method for automatic classification of rail surface defect severity from images collected by rail inspection vehicles. It contains 2 components: a rail surface segmentation module, which utilizes structured random forests to generate an edge map and a Generalized Hough Transform to locate the boundaries of the rail surface; and a defect severity classification module, which combines multiple classifiers through a stacked ensemble model. The first-level learners are trained using descriptors of the rail surface images extracted by texton forests andtexton dictionaries, with x2-kernel SVM classifiers. The probability estimation output of the first-level learners is the input to a second level linear-kernel SVM. Our experiments on a dataset of 939 images categorized into 8 severity levels achieved 82% accuracy.
Simon Laflamme - One of the best experts on this subject based on the ideXlab platform.
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Experimental wind tunnel study of a smart sensing skin for Condition Evaluation of a wind turbine blade
Smart Materials and Structures, 2017Co-Authors: Austin Downey, Simon Laflamme, Filippo UbertiniAbstract:© 2017 IOP Publishing Ltd. Condition Evaluation of wind turbine blades is difficult due to their large size, complex geometry and lack of economic and scalable sensing technologies capable of detecting, localizing, and quantifying faults over a blade's global area. A solution is to deploy inexpensive large area electronics over strategic areas of the monitored component, analogous to sensing skin. The authors have previously proposed a large area electronic consisting of a soft elastomeric capacitor (SEC). The SEC is highly scalable due to its low cost and ease of fabrication, and can, therefore, be used for monitoring large-scale components. A single SEC is a strain sensor that measures the additive strain over a surface. Recently, its application in a hybrid dense sensor network (HDSN) configuration has been studied, where a network of SECs is augmented with a few off-the-shelf strain gauges to measure boundary Conditions and decompose the additive strain to obtain unidirectional surface strain maps. These maps can be analyzed to detect, localize, and quantify faults. In this work, we study the performance of the proposed sensing skin at conducting Condition Evaluation of a wind turbine blade model in an operational environment. Damage in the form of changing boundary Conditions and cuts in the monitored substrate are induced into the blade. An HDSN is deployed onto the interior surface of the substrate, and the blade excited in a wind tunnel. Results demonstrate the capability of the HDSN and associated algorithms to detect, localize, and quantify damage. These results show promise for the future deployment of fully integrated sensing skins deployed inside wind turbine blades for Condition Evaluation.