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Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data
Joint Authors
Meruane, V.
Droguett, Enrique López
Cofre-Martel, Sergio
Kobrich, Philip
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-27, 27 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-08
Country of Publication
Egypt
No. of Pages
27
Main Subjects
Abstract EN
Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis.
However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process.
To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment.
Here, we propose a novel deep CNN-based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process.
These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification.
The approach is validated and exemplified by means of two case studies involving a mass-spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data.
For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.
American Psychological Association (APA)
Cofre-Martel, Sergio& Kobrich, Philip& Droguett, Enrique López& Meruane, V.. 2019. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration،Vol. 2019, no. 2019, pp.1-27.
https://search.emarefa.net/detail/BIM-1211758
Modern Language Association (MLA)
Cofre-Martel, Sergio…[et al.]. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration No. 2019 (2019), pp.1-27.
https://search.emarefa.net/detail/BIM-1211758
American Medical Association (AMA)
Cofre-Martel, Sergio& Kobrich, Philip& Droguett, Enrique López& Meruane, V.. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-27.
https://search.emarefa.net/detail/BIM-1211758
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211758