Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme
In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bay...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/4160 |
| _version_ | 1797603529732390912 |
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| author | Wen Wu Sergio Cantero-Chinchilla Wang-ji Yan Manuel Chiachio Ruano Rasa Remenyte-Prescott Dimitrios Chronopoulos |
| author_facet | Wen Wu Sergio Cantero-Chinchilla Wang-ji Yan Manuel Chiachio Ruano Rasa Remenyte-Prescott Dimitrios Chronopoulos |
| author_sort | Wen Wu |
| collection | DOAJ |
| description | In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three-dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well. |
| first_indexed | 2024-03-11T04:32:27Z |
| format | Article |
| id | doaj.art-d33373df758c4fecb5013649977795a8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:27Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d33373df758c4fecb5013649977795a82023-11-17T21:19:56ZengMDPI AGSensors1424-82202023-04-01238416010.3390/s23084160Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian SchemeWen Wu0Sergio Cantero-Chinchilla1Wang-ji Yan2Manuel Chiachio Ruano3Rasa Remenyte-Prescott4Dimitrios Chronopoulos5Institute for Aerospace Technology, Resilience Engineering Research Group, The University of Nottingham, Nottingham NG7 2RD, UKDepartment of Mechanical Engineering, University of Bristol, Bristol BS8 1TR, UKState Key Laboratory of Internet of Things for Smart City, Department of Civil and Environmental Engineering, University of Macau, Macau 999078, ChinaDepartment of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18001 Granada, SpainResilience Engineering Research Group, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UKDepartment of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), KU Leuven, 9000 Leuven, BelgiumIn this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three-dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well.https://www.mdpi.com/1424-8220/23/8/4160guided wavesjoints/bounded structuresdamage identificationBayesian inferencehybrid wave and finite elementsurrogate model |
| spellingShingle | Wen Wu Sergio Cantero-Chinchilla Wang-ji Yan Manuel Chiachio Ruano Rasa Remenyte-Prescott Dimitrios Chronopoulos Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme Sensors guided waves joints/bounded structures damage identification Bayesian inference hybrid wave and finite element surrogate model |
| title | Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme |
| title_full | Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme |
| title_fullStr | Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme |
| title_full_unstemmed | Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme |
| title_short | Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme |
| title_sort | damage quantification and identification in structural joints through ultrasonic guided wave based features and an inverse bayesian scheme |
| topic | guided waves joints/bounded structures damage identification Bayesian inference hybrid wave and finite element surrogate model |
| url | https://www.mdpi.com/1424-8220/23/8/4160 |
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