Noise cancellation with LMS, NLMS and RLS filtering algorithms to improve the fault detection of an industrial measurement system

Noise cancellation with LMS, NLMS and RLS filtering algorithms to improve the fault detection of an industrial measurement system | 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 Noise cancellation with LMS, NLMS and RLS filtering algorithms to improve the fault detection of an industrial measurement system Tanja Lampl, Abdul Hadi, Abdul Qadir, Juan Manuel Nzamio Mba Andeme This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2639549/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 Adaptive noise cancellation is the process of filtering or estimating a desirable signal from a noise-corrupted observation. This method requires adaptive filters due to the unknown or varying input signal and noise properties. This article provides an overview of the three commonly used adaptive filtering algorithms, namely the Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm. These filtering algorithms are investigated to cancel out the noise effect from noise-corrupted data when no reference signal is available. The algorithms are used to elaborate on a specific problem of industrial measurement systems for fault detection, which is corrupted by the thermal noise in sensors. A comparison is shown among these three adaptive algorithms that perform the best desired estimation and noise-canceling effect under the white noise environment. Three comparison criteria are used to evaluate the performance of these algorithms: the error performance, the rate of convergence and the signal-to-noise ratio (SNR). The simulation results demonstrate that the NLMS algorithm shows an excellent noise-cancelling effect in terms of a lower error performance and a higher SNR at the same convergence speed compared to the LMS and RLS algorithm, which improves the fault detection performance of the industrial measurement system. Noise cancellation Adaptive filters Adaptive filtering algorithms Industrial measurement system Full Text Additional Declarations No competing interests reported. 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. 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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-2639549","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":196757666,"identity":"e6704009-8429-4139-abe8-274c53738d90","order_by":0,"name":"Tanja Lampl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACPhDxAMw8wAiiefgIaWEDEQkMBiAtzCCSh40ELQxsEnARvFrYT6c9SGD4I2/OePhZ5dccOxk2BuaHj27g08KTu90AaIvhzoZjZrdltyUDHcZmbJyD12G52ySAWhg3HDhgdltyGzNQCw+bNF4t/G/BWuw3HDj+rVhyWz0RWiQgtiRuOHDGjPHjtsPEaHkL9IuBcTJQS7E047bjPGzMBPzCz5+77cGHCjnbDTeOb/z4c1u1PT9788PH+LSAQwAcLRIHGJh5QHxm/MqhWsD2NTAw/iCsehSMglEwCkYgAAAEhkVEZStnngAAAABJRU5ErkJggg==","orcid":"","institution":"University of Gävle","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tanja","middleName":"","lastName":"Lampl","suffix":""},{"id":196757667,"identity":"812164f7-cf40-4bb2-af19-ac3bee19c99f","order_by":1,"name":"Abdul Hadi","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Hadi","suffix":""},{"id":196757668,"identity":"7eab495d-e8fb-4dfc-af99-f57bfbfdcb1e","order_by":2,"name":"Abdul Qadir","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Qadir","suffix":""},{"id":196757669,"identity":"e402b1d3-249e-4197-b7dc-36d36c5bedbf","order_by":3,"name":"Juan Manuel Nzamio Mba Andeme","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Manuel Nzamio Mba","lastName":"Andeme","suffix":""}],"badges":[],"createdAt":"2023-02-28 16:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2639549/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2639549/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36538339,"identity":"7168a8db-5ed6-4b94-82c4-1c53fd36c82c","added_by":"auto","created_at":"2023-05-02 18:56:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677297,"visible":true,"origin":"","legend":"","description":"","filename":"NoisecancellationwithLMSNLMSandRLS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2639549/v1_covered_9e4221e5-c5b5-4009-80ee-3daedd41fa24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Noise cancellation with LMS, NLMS and RLS filtering algorithms to improve the fault detection of an industrial measurement system","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Noise cancellation, Adaptive filters, Adaptive filtering algorithms, Industrial measurement system","lastPublishedDoi":"10.21203/rs.3.rs-2639549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2639549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdaptive noise cancellation is the process of filtering or estimating a desirable signal from a noise-corrupted observation. 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