Fault detection for highly coupled signals in the piercing process based on multivariate variational mode decomposition

Fault detection for highly coupled signals in the piercing process based on multivariate variational mode decomposition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fault detection for highly coupled signals in the piercing process based on multivariate variational mode decomposition Wei-Ling Wang, Tsung-Liang Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8578092/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The tremendous success in fault diagnosis of engineering applications with nonstationary signals has been shared for many decades. Most of the signal pre-processing approaches have been studied by many scholars with admirable results. However, for determining a faulty event from multiple signal sources, the research still leaves some gaps that can be filled, especially for the progressive piercing process. In this paper, we have conducted a comprehensive analysis of multi-punch signal processing to accomplish punch condition prediction. We also defined multiple factors and compared the impact of each factor on model performance, including signal decomposition method, feature selection, and machine learning model. Finally, we propose a comprehensive working flow that applies Multivariate Variational Mode Decomposition (MVMD) to decompose highly coupled signals, selects Full-band frequency energy features, and employs Random Forest as the primary regressor, achieving an accuracy of up to 80%. Moreover, we also consider the computation time for data processing and model training to define the working flow. The model that achieves the shortest computation time in our workflow while achieving the best prediction model performance. Piercing process Multi-punch Highly coupled signals Signal decomposition Machine learning Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8578092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574605261,"identity":"3b1b8c15-aea9-48e8-9924-c719e0a21cc3","order_by":0,"name":"Wei-Ling Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei-Ling","middleName":"","lastName":"Wang","suffix":""},{"id":574605262,"identity":"ae014026-1e7f-4f52-9e34-01c7ac5f1d6f","order_by":1,"name":"Tsung-Liang 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