Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105248
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Li, J | - |
dc.creator | He, Y | - |
dc.creator | Li, Q | - |
dc.creator | Zhang, Z | - |
dc.date.accessioned | 2024-04-12T06:51:00Z | - |
dc.date.available | 2024-04-12T06:51:00Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105248 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Li J, He Y, Li Q, Zhang Z. Artificial Intelligence (AI)-Based Evaluation of Bolt Loosening Using Vibro-Acoustic Modulation (VAM) Features from a Combination of Simulation and Experiments. Applied Sciences. 2022; 12(24):12920 is available at https://doi.org/10.3390/app122412920. | en_US |
dc.subject | Acoustic nonlinearity | en_US |
dc.subject | Bolted joint | en_US |
dc.subject | Residual torque | en_US |
dc.subject | Structural health monitoring | en_US |
dc.subject | Support vector regression | en_US |
dc.subject | Vibro-acoustic modulation | en_US |
dc.title | Artificial Intelligence (AI)-based evaluation of bolt loosening using Vibro-Acoustic Modulation (VAM) features from a combination of simulation and experiments | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 24 | - |
dc.identifier.doi | 10.3390/app122412920 | - |
dcterms.abstract | The detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave–surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences of structural and material nonlinearities. A reliable inspection method of residual bolt torque is proposed in this study using support vector regression (SVR) with acoustic features from VAM. By considering the material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), the responses of aluminum–aluminum and composite–composite bolted joints during the VAM test were accurately simulated. The SVRs were subsequently established based on the database built by combining simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses had higher accuracy compared to the conventional nonlinear coefficients. Requiring limited experimental data, the proposed method can achieve a reliable inspection of bolt torque by including the simulated data in the machine training. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, Dec. 2022, v. 12, no. 24, 12920 | - |
dcterms.isPartOf | Applied sciences | - |
dcterms.issued | 2022-12 | - |
dc.identifier.scopus | 2-s2.0-85144905096 | - |
dc.identifier.eissn | 2076-3417 | - |
dc.identifier.artn | 12920 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Shanghai Pujiang Program; Young Elite Scientists Sponsorship Program by CAST; Fundamental Research Funds for the Central Universities from Tongji University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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applsci-12-12920-v2.pdf | 5.72 MB | Adobe PDF | View/Open |
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