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Results clarify the effectiveness of machine learning for optimizing modal testing procedures.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Activity  \n4-1-2024  \nOptimization of Sensor Placement for Modal Testing Using Machine Learning  \nTodd Kelmar  \nSan Jose State University  \nMaria Chierichetti  \nSan Jose State University, [maria.chierichetti@sjsu.edu](maria.chierichetti@sjsu.edu)  \nFatemeh Davoudi Kakhki School of Engineering  \nFollow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca)  \nRecommended Citation  \nTodd Kelmar, Maria Chierichetti, and Fatemeh Davoudi Kakhki. \"Optimization of Sensor Placement for Modal Testing Using Machine Learning\" Applied Sciences (Switzerland) (2024) . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app14073040  \nThis Article is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \napplied sciences  \nArticle  \nOptimization of Sensor Placement for Modal Testing Using Machine Learning  \nTodd Kelmar 1, Maria Chierichetti 1, * and Fatemeh Davoudi Kakhki 2, *  \nCitation: Kelmar, T.; Chierichetti, M.; Davoudi Kakhki, F. Optimization of Sensor Placement for Modal Testing Using Machine Learning. Appl. Sci. 2024, 14, 3040. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app14073040](10.3390/app14073040)  \nAcademic Editors: Mickaël Lallart and Yves Gourinat  \nReceived: 8 February 2024  \nRevised: 28 March 2024  \nAccepted: 29 March 2024  \nPublished: 4 April 2024  \nCopyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Aerospace Engineering, San José State University, San José, CA 95192, USA; [tkelmar@gmail.com](tkelmar@gmail.com)  \n2 Machine Learning & Safety Analytics Lab, School of Engineering, Santa Clara University, Santa Clara, CA 95053, USA  \n* [Correspondence: maria.chierichetti@sjsu.edu](Correspondence: maria.chierichetti@sjsu.edu) (M.C.); [fdavoudikakhki@scu.edu](fdavoudikakhki@scu.edu) (F.D.K.)  \nFeatured Application: This study introduces an innovative approach for optimizing sensor placement in modal testing by applying machine learning with enhanced efficiency and precision.  \nAbstract: Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.  \nKeywords: modal testing; sensor placement; machine learning; finite element method; beam analysis; multifrequency response  \n1. Introduction  \nMechanical structures are subject to vibrations. These vibrations can be internal (such as engine vibration), external (such as turbulence), or a combination of both. Therefore, c","cbCaifdRkuGXc4QM","https://ap.wps.com/l/cbCaifdRkuGXc4QM","pdf",6262565,24,"English","# Introduction\n## Vibration and structural behavior\n## Role and methods of modal testing\n## Sensor placement and excitation inputs\n## Frequency response processing","[{\"question\":\"Why is sensor placement critical in modal testing?\",\"answer\":\"Sensor placement strongly affects the ability to accurately measure the natural frequencies required for validating predicted modal behavior.\"},{\"question\":\"What limitation do conventional sensor placement methodologies have?\",\"answer\":\"They are often time-consuming and rely on iterative procedures for sensor selection.\"},{\"question\":\"How does this study evaluate machine-learning methods for sensor placement?\",\"answer\":\"It compares three machine-learning-based sensor selection approaches using a numerical beam model to simulate real-world modal testing scenarios.\"}]","Optimization of Sensor Placement for Modal Testing Using Machine Learning | PDF"]