Quantitative Optimization and Assessment of Maintenance Rules in Nuclear Power Plants Using Probabilistic Safety Assessment

Quantitative Optimization and Assessment of Maintenance Rules in Nuclear Power Plants Using Probabilistic Safety Assessment | 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 Quantitative Optimization and Assessment of Maintenance Rules in Nuclear Power Plants Using Probabilistic Safety Assessment Guolong Sheng, Jiang Guo, Yuheng Cao, Yixiong Feng, Zhifeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9277786/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The traditional deterministic safety management systems adopted in early nuclear power plants (NPPs) often operate as inflexible frameworks, leading to overly conservative maintenance strategies and a lack of transparency in safety analysis logic.To address the limitations of deterministic approaches, this paper proposes a quantitative optimization and assessment framework for Maintenance Rules (MR) in Pressurized Water Reactors (PWRs) using Probabilistic Safety Assessment (PSA). The methodology integrates risk-informed decision-making to categorize Systems, Structures, and Components (SSCs) based on their risk significance using Fussell-Vesely (F-V) importance and Risk Achievement Worth (RAW). Furthermore, a dynamic baseline threshold approach is established to quantitatively determine plant-specific performance criteria, including the maximum allowed Unavailability (UA) and Maintenance Rule Functional Failures (MRFFs). A case study utilizing real operational data from the Residual Heat Removal System (RRA) and Component Cooling Water System (RRI) validates the proposed framework. The results demonstrate that the risk-informed MR framework effectively optimizes maintenance resource allocation and enhances plant economics without compromising core damage frequency (CDF) or large early release frequency (LERF). This quantitative approach provides a reproducible template for upgrading nuclear safety management systems in existing PWR fleets. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 31 Mar, 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. 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. 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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-9277786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622019433,"identity":"38356880-c599-4c92-8359-5eff7f21232f","order_by":0,"name":"Guolong Sheng","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Guolong","middleName":"","lastName":"Sheng","suffix":""},{"id":622019434,"identity":"4081b77d-b446-43df-84ad-01119a758383","order_by":1,"name":"Jiang Guo","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Guo","suffix":""},{"id":622019435,"identity":"8af80966-82c6-43ce-9847-ae41098ef612","order_by":2,"name":"Yuheng Cao","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yuheng","middleName":"","lastName":"Cao","suffix":""},{"id":622019436,"identity":"06ea5f69-17aa-4fe0-bef6-c44dba1e60b0","order_by":3,"name":"Yixiong Feng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yixiong","middleName":"","lastName":"Feng","suffix":""},{"id":622019437,"identity":"4a7d355f-0510-4405-95bd-fc94bc555d29","order_by":4,"name":"Zhifeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RsYrCMBzH8X8R0iXg+j8O8RUKggiCfZWEQLvIzR0cAkIduwr6FoLzvwQ65bj1hhucOusLiDnBNcRNMN/lR+D/mQIQi71iCAwqt0MA4WYQSKzbD/00yej+DCDj3aY/UfW3OPxQj1DNpU6/yUuSPZUZ2V4diQoEW0rNv4SXDFB0n5faqGmrC0xqIzXyzEsYyhrbq1GTNThyDSAcFcNWm0XG/okOIIgFQ+qMQAtqJrpyUvOln4y3ZY+0MvmwsfL3vJqPmtT6ySOpgYv7Z7Kge1cOkFLocSwWi71ZN7iPRnwl/Mz2AAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-31 09:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9277786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9277786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481211,"identity":"84a7b61b-473e-4b2c-bcea-c25da372f493","added_by":"auto","created_at":"2026-04-22 02:16:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":209936,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9277786/v1_covered_d4caac1f-9d73-4963-a264-5590d8a33f07.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Optimization and Assessment of Maintenance Rules in Nuclear Power Plants Using Probabilistic Safety Assessment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9277786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9277786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The traditional deterministic safety management systems adopted in early nuclear power plants (NPPs) often operate as inflexible frameworks, leading to overly conservative maintenance strategies and a lack of transparency in safety analysis logic.To address the limitations of deterministic approaches, this paper proposes a quantitative optimization and assessment framework for Maintenance Rules (MR) in Pressurized Water Reactors (PWRs) using Probabilistic Safety Assessment (PSA). The methodology integrates risk-informed decision-making to categorize Systems, Structures, and Components (SSCs) based on their risk significance using Fussell-Vesely (F-V) importance and Risk Achievement Worth (RAW). Furthermore, a dynamic baseline threshold approach is established to quantitatively determine plant-specific performance criteria, including the maximum allowed Unavailability (UA) and Maintenance Rule Functional Failures (MRFFs). A case study utilizing real operational data from the Residual Heat Removal System (RRA) and Component Cooling Water System (RRI) validates the proposed framework. The results demonstrate that the risk-informed MR framework effectively optimizes maintenance resource allocation and enhances plant economics without compromising core damage frequency (CDF) or large early release frequency (LERF). 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