Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions

Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions | 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 Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions Xiaolei Pan, Ao Shen, Hongxiao Chen, Kunyi Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6158680/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 15 You are reading this latest preprint version Abstract Most bearing fault diagnosis methods based on transfer learning for variable working conditions typically focus on domain alignment, while neglecting the class information of the samples themselves. This oversight leads to inaccurate class alignment between the source and target domains, thereby reducing diagnostic accuracy. To address this issue, a novel fault diagnosis method based on contrastive learning and domain adaptation network (CDAN) is proposed. Firstly, a deep residual shrinkage network with channel-wise thresholds (DRSN-CWT) is utilized to directly extract features from raw vibration data, thereby maximizing the extraction of relevant features. Subsequently, a feature contrast module, guided by a novel global contrastive loss (GCL), is introduced to quantify the similarity between different extracted feature data distributions, performing contrastive analysis on the extracted features to maximize the distance between samples of different fault classes and minimize the distance between samples of the same fault class. Concurrently, an adversarial domain adaptation module is utilized to learn the discriminative features shared between domains, aligning the data distributions of the source and target domains. Furthermore, an adaptive factor is designed to dynamically balance the relative importance between domain alignment and classification performance, mitigating the adverse impacts caused by overly large or small loss terms. Experimental results on the CWRU and PU bearing datasets validate the effectiveness and superiority of the proposed method, achieving average diagnostic accuracies of 99.64% and 80.25%, respectively. bearing fault diagnosis transfer learning contrastive learning domain adaptation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 30 Apr, 2025 Reviews received at journal 26 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviews received at journal 24 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers invited by journal 12 Mar, 2025 Editor assigned by journal 08 Mar, 2025 Submission checks completed at journal 06 Mar, 2025 First submitted to journal 04 Mar, 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. 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-6158680","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425969017,"identity":"37bdf7e3-c9a9-4035-a9e1-1ade8b2e4bf7","order_by":0,"name":"Xiaolei Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACZiB+wCABYjI+YGwAMyBcvFoSIGqYDYjTwgDWAgZsEowNDIS1GBxnfvggocKCwZz97LHKnzss5MwZmA/e5mGwy8OlRbKZzdgg4YwEg2VPXtpt3jMSxpYNbMnWPAzJxbi08DMzmEkktkkwGBzIMbvN2CaRuOEAj5k0D8OBxAYcWtiY2b9JJP4Dajn/xqzwZ5tE/YYD/N/wauFn5gHaAgwogxs5Zgy8bRIJBgd42PBqkWzmKTZIOCbBYznjjbE0UIvhTqDvLOcYJOPUYnD++MYHH2rq5Mz5cww//myrkzdnb354402FHU4tMMBjADeEGUwSUI+ihhjFo2AUjIJRMLIAAJyWSvdMXkWYAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":true,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Pan","suffix":""},{"id":425969018,"identity":"bbec1af8-10d9-40ef-8621-9d3fbcb9e02b","order_by":1,"name":"Ao Shen","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Shen","suffix":""},{"id":425969019,"identity":"0586d178-763f-424e-b4de-3bd3aa1c0fc9","order_by":2,"name":"Hongxiao Chen","email":"","orcid":"","institution":"China Huadian Corporation LTD. Zhejiang Company","correspondingAuthor":false,"prefix":"","firstName":"Hongxiao","middleName":"","lastName":"Chen","suffix":""},{"id":425969021,"identity":"68597fda-d7f4-47ef-a695-3f71ebfa7a78","order_by":3,"name":"Kunyi Wu","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Kunyi","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-05 04:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6158680/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6158680/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02946-w","type":"published","date":"2026-01-17T16:30:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100614683,"identity":"dfed9215-5235-43c5-87ca-c6e5fed3f912","added_by":"auto","created_at":"2026-01-19 17:23:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4495145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6158680/v1_covered_601d7a5e-fc03-4ce5-87c1-3bf2241c52b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions","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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bearing fault diagnosis, transfer learning, contrastive learning, domain adaptation","lastPublishedDoi":"10.21203/rs.3.rs-6158680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6158680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Most bearing fault diagnosis methods based on transfer learning for variable working conditions typically focus on domain alignment, while neglecting the class information of the samples themselves. This oversight leads to inaccurate class alignment between the source and target domains, thereby reducing diagnostic accuracy. To address this issue, a novel fault diagnosis method based on contrastive learning and domain adaptation network (CDAN) is proposed. Firstly, a deep residual shrinkage network with channel-wise thresholds (DRSN-CWT) is utilized to directly extract features from raw vibration data, thereby maximizing the extraction of relevant features. Subsequently, a feature contrast module, guided by a novel global contrastive loss (GCL), is introduced to quantify the similarity between different extracted feature data distributions, performing contrastive analysis on the extracted features to maximize the distance between samples of different fault classes and minimize the distance between samples of the same fault class. Concurrently, an adversarial domain adaptation module is utilized to learn the discriminative features shared between domains, aligning the data distributions of the source and target domains. Furthermore, an adaptive factor is designed to dynamically balance the relative importance between domain alignment and classification performance, mitigating the adverse impacts caused by overly large or small loss terms. Experimental results on the CWRU and PU bearing datasets validate the effectiveness and superiority of the proposed method, achieving average diagnostic accuracies of 99.64% and 80.25%, respectively.","manuscriptTitle":"Bearing fault diagnosis method based on contrastive learning and domain adaptation under variable working conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 17:46:52","doi":"10.21203/rs.3.rs-6158680/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-28T07:56:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-30T08:17:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-26T14:30:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318647335864561276202577352959489615721","date":"2025-04-14T04:17:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309274809608777952867063434496555458868","date":"2025-04-14T04:11:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T08:53:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T11:14:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227026659624819519032684276807529997506","date":"2025-03-13T08:01:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25636317235421744918255692570634020980","date":"2025-03-12T14:05:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14554306318622086586744088816422440723","date":"2025-03-12T13:36:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173044665975797423474731005677838199049","date":"2025-03-12T13:33:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-12T13:13:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-08T12:31:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-06T10:07:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Machine Learning and Cybernetics","date":"2025-03-05T04:14:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d4d13a8f-4678-419e-9ca8-565c520ab7db","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:48:22+00:00","versionOfRecord":{"articleIdentity":"rs-6158680","link":"https://doi.org/10.1007/s13042-025-02946-w","journal":{"identity":"international-journal-of-machine-learning-and-cybernetics","isVorOnly":false,"title":"International Journal of Machine Learning and Cybernetics"},"publishedOn":"2026-01-17 16:30:04","publishedOnDateReadable":"January 17th, 2026"},"versionCreatedAt":"2025-03-10 17:46:52","video":"","vorDoi":"10.1007/s13042-025-02946-w","vorDoiUrl":"https://doi.org/10.1007/s13042-025-02946-w","workflowStages":[]},"version":"v1","identity":"rs-6158680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6158680","identity":"rs-6158680","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}