MARC details
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02923nam a2200253Ia 4500 |
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MX-MdCICY |
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20250625162433.0 |
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CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
B-19560 |
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250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT |
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| Title |
A transfer learning approach for damage diagnosis in composite laminated plate using Lamb waves |
| 490 0# - SERIES STATEMENT |
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| Volume/sequential designation |
Smart Materials and Structures, 31(6), p.065002, 2022 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Lamb wave-based damage diagnosis systems are widely regarded as a likely candidate for real-time structural health monitoring (SHM), although analysing the Lamb wave response is still a challenging task due to its complex physics. Recently, deep learning (DL)models such as convolutional neural network (CNN)have shown robust classification performance in various structures using Lamb wave based diagnostic strategies. However, these DL models are often designed to address isolated tasks, which means that the model needs to be re-trained from scratch to accommodate any small change to the setup. Thus, such data-dependency of the DL model designed for the SHM system can restrict its full usage. This paper presents a study on a version of the transfer learning framework (TLF)based on 1D-CNN autoencoder and a classifier as a possible way to address this problem. In the transfer learning approach, the knowledge learned by a network represented as source model, while performing one or more tasks is utilized to improve the damage diagnosing ability of another network represented as target model operating under other conditions. In TLF, a ResNet autoencoder model will selectively outsource its pre-trained layers to a separate 1D-CNN model, which is a supervised learning model aimed to perform tasks, such as classification. In order to train both the source model and the target model, two separate databases are constructed using the Open Guided Waves diagnostic data repository containing scanned Lamb wave signals generated from a 2 mm thin carbon fibre-reinforced polymer (CFRP)plate structure, in which a range of frequencies and artificial defects are used. A TLF variant which includes transferred layers of pretrained ResNet autoencoder and 1D CNN classifier, have been developed, trained and tested with an unseen database containing 144 samples. Based on the test performance, the adopted version of TLF achieved an impressive 82.64 percent accuracy and emerged as the most robust, balanced and computationally more economical classification model. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
STRUCTURAL HEALTH MONITORING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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TRANSFER LEARNING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
LAMB WAVE |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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1D CNN CLASSIFIER |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
RESNET AUTOENCODER |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
DAMAGE DETECTION |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Rai, A. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Mitra, M. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1HiieQw-4CrSwtpO1Y9X7bZwMZT14baYs/view?usp=drivesdk">https://drive.google.com/file/d/1HiieQw-4CrSwtpO1Y9X7bZwMZT14baYs/view?usp=drivesdk</a> |
| Public note |
Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
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Clasificación local |
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Documentos solicitados |
