Digital Twin-Driven Edge–Cloud Collaborative Remote Fault Diagnosis and Intelligent Predictive Maintenance for Critical Hydropower Equipment

Digital Twin-Driven Edge–Cloud Collaborative Remote Fault Diagnosis and Intelligent Predictive Maintenance for Critical Hydropower Equipment | 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 Digital Twin-Driven Edge–Cloud Collaborative Remote Fault Diagnosis and Intelligent Predictive Maintenance for Critical Hydropower Equipment Dong Yang, Zhile Jiang, Kai Zheng, Yuxuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9410185/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract To address the problems of lagging remote monitoring, complex fault mechanisms, and inefficient maintenance decisions for key equipment in hydropower stations, this paper proposes a remote diagnosis and intelligent maintenance method based on edge-cloud collaboration and digital twin-driven approaches. A layered architecture encompassing equipment, perception, transmission, analysis, and service layers is constructed. A physical-geometric-behavior coupled digital twin model is established, and PLC-SCADA control systems, multi-source monitoring data, fault diagnosis models, and remaining life prediction methods are integrated to achieve a closed loop of equipment status perception, anomaly identification, degradation assessment, and maintenance optimization. Experimental verification is conducted on a dual-unit Pelton hydropower system. Results show that the proposed method exhibits higher fault diagnosis accuracy, better life prediction performance, and superior maintenance economy under complex operating conditions, providing effective technical support for the health management of key equipment in intelligent hydropower stations. Key equipment for hydropower stations digital twins edge-cloud collaboration remote fault diagnosis predictive maintenance remaining life prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 29 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 13 Apr, 2026 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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