Acoustic emission diagnosis of lubricant water contamination fault by using adaptive multi-fidelity Bayesian optimization

Acoustic emission diagnosis of lubricant water contamination fault by using adaptive multi-fidelity Bayesian optimization | 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 Acoustic emission diagnosis of lubricant water contamination fault by using adaptive multi-fidelity Bayesian optimization Ziyang Zhu, Jiaojiao Ma, Dongming Xiao, Fengshou Gu, Xuejun Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7801677/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Tribology Letters → Version 1 posted 7 You are reading this latest preprint version Abstract In rotating machinery, water contamination in lubricants can lead to oil film failure and exacerbate equipment wear. The acoustic emission (AE) technology demonstrates superior performance over traditional detection methods, such as spectral and vibration analyses, for early fault detection due to its sensitivity to weak signals. This paper introduces an adaptive multi-fidelity Bayesian optimization approach for detecting water contamination in lubricants. The technique utilizes a detection index (DI) in conjunction with the interquartile range (IQR) statistical filtering for denoising AE signals, thereby effectively improving the signal-to-noise ratio. An adaptive penalty parameter, dynamically adjusted with each training epoch, is designed to regulate the hyperparameters of the Bayesian optimized convolutional neural network (CNN), including learning rate, mini-batch size, and training epochs. Experimental results demonstrate that the proposed method effectively denoises AE signals and achieves a high recognition accuracy of 99.15% for the lubricant water contamination fault. Compared to CNNs without hyperparameter optimization, the accuracy improves by 18.48%, while computational time reduces by 7.35% relative to the Bayesian optimization without the adaptive penalty parameter. This approach achieves an optimal balance between the rapid exploration and robust convergence, enabling more efficient hyperparameter optimization and reliable identification of lubricant water contamination across varying moisture levels. Lubricant water contamination Acoustic emission signals Bayesian optimization Multi-fidelity Full Text Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstracts.docx Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Tribology Letters → Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 07 Oct, 2025 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-7801677","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529785851,"identity":"6ad98558-4c52-4272-8768-3000bd0f4e6b","order_by":0,"name":"Ziyang Zhu","email":"","orcid":"","institution":"Foshan University","correspondingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Zhu","suffix":""},{"id":529785853,"identity":"385f0b94-be4d-4ae4-945e-1fb3b1537cdc","order_by":1,"name":"Jiaojiao 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","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tribology-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tril","sideBox":"Learn more about [Tribology Letters](https://www.springer.com/journal/11249)","snPcode":"11249","submissionUrl":"https://submission.nature.com/new-submission/11249/3","title":"Tribology Letters","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer 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The acoustic emission (AE) technology demonstrates superior performance over traditional detection methods, such as spectral and vibration analyses, for early fault detection due to its sensitivity to weak signals. This paper introduces an adaptive multi-fidelity Bayesian optimization approach for detecting water contamination in lubricants. The technique utilizes a detection index (DI) in conjunction with the interquartile range (IQR) statistical filtering for denoising AE signals, thereby effectively improving the signal-to-noise ratio. An adaptive penalty parameter, dynamically adjusted with each training epoch, is designed to regulate the hyperparameters of the Bayesian optimized convolutional neural network (CNN), including learning rate, mini-batch size, and training epochs. Experimental results demonstrate that the proposed method effectively denoises AE signals and achieves a high recognition accuracy of 99.15% for the lubricant water contamination fault. Compared to CNNs without hyperparameter optimization, the accuracy improves by 18.48%, while computational time reduces by 7.35% relative to the Bayesian optimization without the adaptive penalty parameter. This approach achieves an optimal balance between the rapid exploration and robust convergence, enabling more efficient hyperparameter optimization and reliable identification of lubricant water contamination across varying moisture levels.\u003c/p\u003e","manuscriptTitle":"Acoustic emission diagnosis of lubricant water contamination fault by using adaptive multi-fidelity Bayesian optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:32:04","doi":"10.21203/rs.3.rs-7801677/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T23:14:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T13:43:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142614142255025455492983469836970545275","date":"2025-10-13T12:28:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T08:55:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T15:21:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T15:21:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tribology Letters","date":"2025-10-07T17:07:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tribology-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tril","sideBox":"Learn more about [Tribology Letters](https://www.springer.com/journal/11249)","snPcode":"11249","submissionUrl":"https://submission.nature.com/new-submission/11249/3","title":"Tribology Letters","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"504c46e7-504b-4a47-995b-4181461cc4ce","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:01:22+00:00","versionOfRecord":{"articleIdentity":"rs-7801677","link":"https://doi.org/10.1007/s11249-025-02097-7","journal":{"identity":"tribology-letters","isVorOnly":false,"title":"Tribology Letters"},"publishedOn":"2025-12-01 15:57:38","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-10-27 11:32:04","video":"","vorDoi":"10.1007/s11249-025-02097-7","vorDoiUrl":"https://doi.org/10.1007/s11249-025-02097-7","workflowStages":[]},"version":"v1","identity":"rs-7801677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7801677","identity":"rs-7801677","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}