Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model | 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 Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA Model Longyi Liu, Yanqing Zhao, Yi Hu, Yongze Ma, Xiyang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4635315/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In response to the challenges posed by imbalanced failure diagnosis samples, limited labeled data, and significant computational costs in actual industrial production settings, this paper introduces a high-precision, low-resource, end-to-end fault diagnosis framework. On one hand, we propose a data augmentation method based on GCGAN, which combines CNN and GRU to construct core network structures for the generator and discriminator. We integrate a novel Smoothed Hinge-Cross-Entropy loss function to facilitate the training process, effectively mitigating mode collapse and vanishing gradient issues. On the other hand, we design a lightweight fault diagnosis model based on MDSCNN-ICA-BiGRU. By substituting standard convolutions with depthwise separable convolutions on deeper channels, the model complexity is significantly reduced, facilitating effective extraction of multiscale spatial features. The improved Coordinate Attention (CA) mechanism filters out noise and enhances the extraction of high-frequency characteristics. Combined with BiGRU, the model captures global temporal associations, achieving a fusion of spatiotemporal features. Experimental results demonstrate that the proposed approach performs well on both publicly available simulation datasets and private laboratory datasets. Compared to other benchmark methods, the GCGAN module significantly enhances data augmentation, improving classification accuracy on CNNs by 10%. When compared with classic convolutional networks such as DRSN and WDCNN, our MDSCNN-ICA-BiGRU shows faster and more stable convergence rates, with near-100% accuracy on test sets and an average computation cost reduction of approximately 70%. Even in noisy environments, our method maintains high accuracy with a slow rate of precision decay, indicating robustness and generalization capabilities. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.zip Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Dec, 2024 Reviews received at journal 30 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviews received at journal 29 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor assigned by journal 01 Aug, 2024 Editor invited by journal 27 Jun, 2024 Submission checks completed at journal 26 Jun, 2024 First submitted to journal 25 Jun, 2024 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. 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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-4635315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326645483,"identity":"defbd0d2-c3d4-472e-a5b1-5b3c1acac21f","order_by":0,"name":"Longyi Liu","email":"","orcid":"","institution":"Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang","correspondingAuthor":false,"prefix":"","firstName":"Longyi","middleName":"","lastName":"Liu","suffix":""},{"id":326645485,"identity":"8fa79a9a-5b33-41fd-85a4-a48cb3bb1ed8","order_by":1,"name":"Yanqing Zhao","email":"","orcid":"","institution":"Shenyang Institute of 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