[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118395-en":3,"doc-seo-118395-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118395,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Optimization of Sensor Placement for Modal Testing Using Machine Learning","Modal testing plays a central role in aerostructure design by validating natural frequencies and mode shapes predicted via computational models. Accurate sensor placement is essential to obtain reliable measurements, yet traditional selection methods are often iterative and time-intensive. This study investigates machine-learning-driven sensor selection, proposing three learning-based approaches and benchmarking their efficiency against established techniques. A numerical beam model supports evaluation of modal testing scenarios, demonstrating how machine learning can improve efficiency and precision in sensor placement workflows.","Santa Clara University  \nScholar Commons  \n\n| General Engineering | School of Engineering |\n| --- | --- |\n| 3-29-2024\u003Cbr>Optimization of Sensor Placement for Modal Testing Using Machine Learning\u003Cbr>Todd Kelmar\u003Cbr>Maria Chierichetti\u003Cbr>Fatemeh Davoudi Kakhki\u003Cbr>Santa Clara University, [fdavoudikakhki@scu.edu](fdavoudikakhki@scu.edu)\u003Cbr>Follow this and additional works at: [https://scholarcommons.scu.edu/eng_grad](https://scholarcommons.scu.edu/eng_grad) |  |\n\nRecommended Citation  \nKelmar, T., Chierichetti, M., & Davoudi Kakhki, F. (2024) . Optimization of Sensor Placement for Modal Testing Using Machine Learning. Applied Sciences, 14(7), Article 7. [https://doi.org/10.3390/app14073040](https://doi.org/10.3390/app14073040)  \n[2.7](2.7)) .  \nThis Article is brought to you for free and open access by the School of Engineering at Scholar Commons. It has been accepted for inclusion in General Engineering by an authorized administrator of Scholar Commons. For more information, please contact [rscroggin@scu.edu](rscroggin@scu.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, characterizing the behavior of a system under vibration or other dynamic f","cbCaiez9dgk02TMv","https://ap.wps.com/l/cbCaiez9dgk02TMv","pdf",5422962,1,24,"English","en",105,"# Introduction\n## Sensor placement in modal testing\n# Machine learning for sensor selection\n## Proposed learning-based approaches\n# Numerical evaluation using a beam model\n## Efficiency comparison and results","[{\"question\":\"Why is sensor placement critical in modal testing?\",\"answer\":\"Sensor placement strongly affects the measured natural frequencies and mode shapes. Careful selection is required to ensure the test accurately captures the intended structural dynamics.\"},{\"question\":\"What does the study propose for optimizing sensor placement?\",\"answer\":\"The study applies machine learning to sensor selection, introducing three machine learning-based approaches. It compares their efficiencies with established techniques.\"},{\"question\":\"How is the approach evaluated?\",\"answer\":\"Evaluation uses a numerical model of a beam to simulate realistic modal testing scenarios. The results assess how effectively machine learning improves efficiency and precision.\"}]","Optimization of Sensor Placement for Modal Testing Using Machine Learning | PDF",1785683424,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimization-of-sensor-placement-for-modal-testing-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimization-of-sensor-placement-for-modal-testing-using-machine-learning/118395/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is sensor placement critical in modal testing?","Question",{"text":75,"@type":76},"Sensor placement strongly affects the measured natural frequencies and mode shapes. 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