Wind Turbine Gearbox Fault Diagnosis Based on PNCC-STFT Voiceprint Feature Extraction and GDC- ResNet

Wind Turbine Gearbox Fault Diagnosis Based on PNCC-STFT Voiceprint Feature Extraction and GDC- ResNet | 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 Wind Turbine Gearbox Fault Diagnosis Based on PNCC-STFT Voiceprint Feature Extraction and GDC- ResNet Geyang Zhao, Lida Liao, Bin Huang, Chuanjiang Liu, Cheng Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8004621/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Due to harsh operating environments and wear caused by long-term use, wind turbine gearboxes often suffer from various faults, which lead to the emission of different noises or abnormal sounds during operation. This seriously affects the normal operation of wind turbines and their power generation efficiency. Given the limitations of traditional diagnostic methods in engineering practice, this paper proposes a fault diagnosis method for wind turbine gearboxes, which integrates the Residual Neural Network with a Global Dynamic Channel Coordinate Attention Module (GDC-ResNet) and the PNCC-STFT voiceprint feature extraction combining Power Normalized Cepstral Coefficients (PNCC) and Short-Time Fourier Transform (STFT). This method is specifically developed to address the issues of feature extraction from gear noise signals and intelligent diagnosis in noisy environments. First, noise data of wind turbine gearboxes under six different states were collected using an LMS noise acquisition instrument and noise sensors. Subsequently, the PNCC-STFT voiceprint feature extraction method was employed to extract voiceprint spectrograms from the gearbox noise signals. Given that the ResNet baseline model exhibits low classification accuracy for gearbox voiceprints, a high-performance gear fault diagnosis model, GDC-ResNet, is proposed by incorporating an attention mechanism and dynamic convolutional kernels into ResNet. Experimental results show that the GDC-ResNet model achieves a classification accuracy of 99.3% for the PNCC-STFT voiceprints of gearboxes, representing an improvement of 1.8% compared with the ResNet baseline model. The proposed classification model can effectively classify gearboxes with variable rotational speeds under different states in the dataset, enabling more efficient diagnosis of faults in wind turbine gearboxes. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Wind turbine gearbox Feature extraction GDC-Resnet PNCC-STFT Noise signal Fault diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 14 Nov, 2025 Editor assigned by journal 08 Nov, 2025 Submission checks completed at journal 08 Nov, 2025 First submitted to journal 01 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-8004621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550931287,"identity":"31b93a66-6c20-42cd-9fcf-5f5fdba34bd1","order_by":0,"name":"Geyang Zhao","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Geyang","middleName":"","lastName":"Zhao","suffix":""},{"id":550931288,"identity":"6f4b77c1-c82f-497b-9951-bc31bbcb5726","order_by":1,"name":"Lida 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extraction, GDC-Resnet, PNCC-STFT, Noise signal, Fault diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8004621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8004621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to harsh operating environments and wear caused by long-term use, wind turbine gearboxes often suffer from various faults, which lead to the emission of different noises or abnormal sounds during operation. This seriously affects the normal operation of wind turbines and their power generation efficiency. Given the limitations of traditional diagnostic methods in engineering practice, this paper proposes a fault diagnosis method for wind turbine gearboxes, which integrates the Residual Neural Network with a Global Dynamic Channel Coordinate Attention Module (GDC-ResNet) and the PNCC-STFT voiceprint feature extraction combining Power Normalized Cepstral Coefficients (PNCC) and Short-Time Fourier Transform (STFT). This method is specifically developed to address the issues of feature extraction from gear noise signals and intelligent diagnosis in noisy environments. First, noise data of wind turbine gearboxes under six different states were collected using an LMS noise acquisition instrument and noise sensors. Subsequently, the PNCC-STFT voiceprint feature extraction method was employed to extract voiceprint spectrograms from the gearbox noise signals. Given that the ResNet baseline model exhibits low classification accuracy for gearbox voiceprints, a high-performance gear fault diagnosis model, GDC-ResNet, is proposed by incorporating an attention mechanism and dynamic convolutional kernels into ResNet. Experimental results show that the GDC-ResNet model achieves a classification accuracy of 99.3% for the PNCC-STFT voiceprints of gearboxes, representing an improvement of 1.8% compared with the ResNet baseline model. The proposed classification model can effectively classify gearboxes with variable rotational speeds under different states in the dataset, enabling more efficient diagnosis of faults in wind turbine gearboxes.\u003c/p\u003e","manuscriptTitle":"Wind Turbine Gearbox Fault Diagnosis Based on PNCC-STFT Voiceprint Feature Extraction and GDC- ResNet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 06:20:50","doi":"10.21203/rs.3.rs-8004621/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-29T15:33:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T03:00:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T11:20:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T09:41:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144496300456416892387115043275987640121","date":"2025-12-02T09:20:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T04:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119078322283221217152571974391338851362","date":"2025-12-01T09:54:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167524985610149075396027925483296571327","date":"2025-11-26T05:38:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66186491486147704212912218921549867582","date":"2025-11-26T05:33:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T02:03:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96602449631021286378623503057684390981","date":"2025-11-26T01:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-26T00:23:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T08:26:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-08T12:37:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-08T12:36:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-01T09:30:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f87ce7e3-0896-4c16-a247-416f1fefab96","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58623251,"name":"Physical sciences/Energy science and technology"},{"id":58623252,"name":"Physical sciences/Engineering"},{"id":58623253,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-29T15:38:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 06:20:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8004621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8004621","identity":"rs-8004621","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}