Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins

Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins | 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 Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins ShengWen Zhou, Li Zhang, Xiaoming Yang, Ruiping Luo, BaiGang Du, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5976822/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract To address challenges in extracting health indicator (HI) curves and making accurate predictions with limited datasets in mechanical system prognostics, this study proposes a digital twin (DT)-driven framework for estimating remaining useful life (RUL). To minimize the deviation between simulated and measured data, we introduce a finite element model correction method using a stacked autoencoder–long short-term memory (SAE–LSTM) network. To reduce reliance on manual expertise and prior knowledge, the LSTM network is used to directly extract features from the frequency-domain vibration data and construct initial HI curves representing equipment performance degradation. Finally, this study employs a relevance vector machine (RVM) model to predict the HI curve trend by integrating failure criteria with twin data to establish the failure threshold. Experimental validation using the PHM2012 public dataset showed that the DT-based RUL prediction reduces the average relative error by 5.4% compared with traditional RUL prediction methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Digital twins Centrifugal pump Rolling bearings Health indicators Remaining useful life Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 23 May, 2025 Reviews received at journal 22 May, 2025 Reviews received at journal 04 May, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 06 Apr, 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. 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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-5976822","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447163050,"identity":"b9413f02-3063-44eb-b83c-7bca066583a7","order_by":0,"name":"ShengWen Zhou","email":"","orcid":"","institution":"Hubei Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"ShengWen","middleName":"","lastName":"Zhou","suffix":""},{"id":447163051,"identity":"6c5a72c8-23c9-447d-8344-00e1bbae219a","order_by":1,"name":"Li 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