A Low-Speed Heavy Load Bearing Fault Diagnosis Method Based on Interpretable Few-Shot Learning

A Low-Speed Heavy Load Bearing Fault Diagnosis Method Based on Interpretable Few-Shot Learning | 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 A Low-Speed Heavy Load Bearing Fault Diagnosis Method Based on Interpretable Few-Shot Learning Jie Bai, Xianbin Sun, Liming Zhang, Xin Zheng, Peihan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8055845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Low-speed heavy-load bearings are widely used in engineering; however, their low-speed characteristics make fault feature extraction challenging, and reproducible fault samples are scarce. Furthermore, the "black-box" nature of deep learning models presents a significant challenge in constructing high-accuracy and interpretable fault diagnosis models when sample data is limited. To address these issues, this paper proposes a lightweight fault diagnosis method for low-speed, heavy-load bearings based on a few-shot learning strategy and interpretability. First, an efficient few-shot learning model, SATNet, is constructed based on the N-shot K-way few-shot learning strategy, incorporating a self-attention mechanism and optimized specifically for the complex fault features of low-speed, heavy-load bearings. Then, the Teager energy operator-based signal enhancement algorithm is applied to preprocess the acoustic emission and vibration signals of the low-speed, heavy-load bearings in both normal and fault conditions. This step extracts the signals' nonlinear and energy features and obtains corresponding time-domain features. The preprocessed acoustic emission and vibration signals are input into the SATNet model for training and testing, accurately classifying faults under different operating conditions. Finally, the SHAP and LIME algorithms are employed to conduct an interpretability analysis of the SATNet model's diagnostic results, thoroughly exploring and revealing the key time-domain features that influence classification decisions and their importance. Experimental results show that the proposed innovative diagnostic method significantly improves both diagnostic accuracy and stability for low-speed, heavy-load bearing fault diagnosis. Low-speed heavy load bearings Fault diagnosis Few-shot learning Interpretability analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviews received at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 18 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 07 Nov, 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-8055845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547899863,"identity":"88d92b84-71e0-4ee4-b248-82f4f2ba38da","order_by":0,"name":"Jie Bai","email":"","orcid":"","institution":"Jilin College of Electronic Information","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Bai","suffix":""},{"id":547899864,"identity":"c496126e-82c4-48a6-ac7e-b58568d85623","order_by":1,"name":"Xianbin 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Furthermore, the \"black-box\" nature of deep learning models presents a significant challenge in constructing high-accuracy and interpretable fault diagnosis models when sample data is limited. To address these issues, this paper proposes a lightweight fault diagnosis method for low-speed, heavy-load bearings based on a few-shot learning strategy and interpretability. First, an efficient few-shot learning model, SATNet, is constructed based on the N-shot K-way few-shot learning strategy, incorporating a self-attention mechanism and optimized specifically for the complex fault features of low-speed, heavy-load bearings. Then, the Teager energy operator-based signal enhancement algorithm is applied to preprocess the acoustic emission and vibration signals of the low-speed, heavy-load bearings in both normal and fault conditions. This step extracts the signals' nonlinear and energy features and obtains corresponding time-domain features. The preprocessed acoustic emission and vibration signals are input into the SATNet model for training and testing, accurately classifying faults under different operating conditions. Finally, the SHAP and LIME algorithms are employed to conduct an interpretability analysis of the SATNet model's diagnostic results, thoroughly exploring and revealing the key time-domain features that influence classification decisions and their importance. Experimental results show that the proposed innovative diagnostic method significantly improves both diagnostic accuracy and stability for low-speed, heavy-load bearing fault diagnosis.\u003c/p\u003e","manuscriptTitle":"A Low-Speed Heavy Load Bearing Fault Diagnosis Method Based on Interpretable Few-Shot Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 07:28:31","doi":"10.21203/rs.3.rs-8055845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T16:19:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T08:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131190298359783751755431938388760397857","date":"2026-01-13T13:30:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-13T05:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277284139719688069438007809303547560115","date":"2026-01-13T02:54:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96602449631021286378623503057684390981","date":"2026-01-12T23:55:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T08:14:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-21T09:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129564821750153316708116830840678990458","date":"2025-12-17T06:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116769829841262559761498499041502425197","date":"2025-12-11T15:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T05:55:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T09:00:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T08:58:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-11-07T10:05:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"524e065b-21a0-4bc3-8eb6-45476c388b0b","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T17:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 07:28:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8055845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8055845","identity":"rs-8055845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}