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Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data
المؤلفون المشاركون
Meruane, V.
Droguett, Enrique López
Cofre-Martel, Sergio
Kobrich, Philip
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-27، 27ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-09-08
دولة النشر
مصر
عدد الصفحات
27
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1211758
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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