Optimization of VMD-CNN-BiLSTM Rolling Bearing Fault Diagnosis Model Based on Improved DBO Algorithm

Optimization of VMD-CNN-BiLSTM Rolling Bearing Fault Diagnosis Model Based on Improved DBO Algorithm | 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 Article Optimization of VMD-CNN-BiLSTM Rolling Bearing Fault Diagnosis Model Based on Improved DBO Algorithm Weiqing Sun, Yue Wang, Xingyi You, Di Zhang, Jingyi Zhang, Xiaohu Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4186362/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 Rolling bearings are important components in mechanical equipment, but they are also a component with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer algorithm(DBO) optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory(VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search; Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal; Then, CNN-BiLSTM was used as the model for fault classification, and the hyperparameters of the model were optimized using the CSADBO algorithm; Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide theoretical reference for other related fault diagnosis problems. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computer science 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. 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-4186362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291479234,"identity":"0d846bf6-5ec9-4c2a-8a0d-a133accae9cf","order_by":0,"name":"Weiqing Sun","email":"","orcid":"","institution":"National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Weiqing","middleName":"","lastName":"Sun","suffix":""},{"id":291479235,"identity":"6946a82c-379c-4dcc-9764-54e4175a2068","order_by":1,"name":"Yue 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Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer algorithm(DBO) optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory(VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search; Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal; Then, CNN-BiLSTM was used as the model for fault classification, and the hyperparameters of the model were optimized using the CSADBO algorithm; Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide theoretical reference for other related fault diagnosis problems.","manuscriptTitle":"Optimization of VMD-CNN-BiLSTM Rolling Bearing Fault Diagnosis Model Based on Improved DBO Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 14:55:07","doi":"10.21203/rs.3.rs-4186362/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d80e0f3c-2d99-4640-b04a-ebf6158b74e6","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30705373,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":30705374,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":30705375,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-06-14T05:36:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-16 14:55:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4186362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4186362","identity":"rs-4186362","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}