Risk-Informed Life Extension Decisions Using Unstructured Maintenance Data

Risk-Informed Life Extension Decisions Using Unstructured Maintenance Data | 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 Risk-Informed Life Extension Decisions Using Unstructured Maintenance Data Venkata Sai Prashanth Sudula, Gopinath Chattopadhyay, Jo-Ann Larkins This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6677317/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Life extension (LE) decision of complex, long-life, capital-intensive assets is a challenge due to availability of structured maintenance data. Data is often unstructured and text-based, making it difficult to perform systematic reliability assessments useful for assessing the remaining useful life (RUL). This study presents a methodology for addressing those challenges by using Natural Language Processing (NLP), NESTOR. Historical maintenance records (2012–2017) from the University of North Dakota (UND) Aviation Academy were analyzed for, three critical engine components: intake gaskets, rocker cover gaskets and baffles in single and twin-engine fleet. Failure Mode and Effect Analysis (FMEA) was conducted for the identification and prioritization of intervention options for failure modes based on likelihood and consequences. The results demonstrate the potential of NLP techniques to support reliability analysis for RUL required for risk informed LE decisions of capital-intensive long life complex assets. Life Extension Failure Mode and Effects Analysis Natural Language Processing Technical Language Processing Unstructured Maintenance Data Risk Priority Number Aviation Maintenance Analytics Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-6677317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466452070,"identity":"b170cd5f-1a9e-4209-8657-f6794ad682a1","order_by":0,"name":"Venkata Sai Prashanth Sudula","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYNACNgYZBvYGIMPAgngtPAw8B0BaJEjRIpEAYhGhxeD82cOfecoO8/DPfH51w48CCQb+9u4E/Fpu5KVJ85w7zCNxO6fsZg/QYRJnzm7Aq0VyBo8ZM2/bYR6G2zlpN3iAWgwkcglo6T9j/BmkRf7mmbSbf4jRws+QYyAN0mJwg/3YbaJs4ZfIS5Occy6dx/BMDtttGQMJHoJ+YeM/e/jDmzJrObnjx5/dfPPHRo6/vRe/FgZgjMAYBihcYrSwPyBC9SgYBaNgFIxEAACjwUM3JLf2wAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-8704-297X","institution":"Federation University Australia - Gippsland Campus","correspondingAuthor":true,"prefix":"","firstName":"Venkata","middleName":"Sai Prashanth","lastName":"Sudula","suffix":""},{"id":466452071,"identity":"628dad1c-3929-4a1b-8363-0316bc069e77","order_by":1,"name":"Gopinath Chattopadhyay","email":"","orcid":"","institution":"Federation University Australia - Gippsland Campus","correspondingAuthor":false,"prefix":"","firstName":"Gopinath","middleName":"","lastName":"Chattopadhyay","suffix":""},{"id":466452072,"identity":"5c2d05c2-ed4d-4cf9-8b29-52c344dac5cb","order_by":2,"name":"Jo-Ann Larkins","email":"","orcid":"","institution":"Federation University Australia - Gippsland Campus","correspondingAuthor":false,"prefix":"","firstName":"Jo-Ann","middleName":"","lastName":"Larkins","suffix":""}],"badges":[],"createdAt":"2025-05-16 05:37:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6677317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6677317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88159285,"identity":"1fb83362-ce4a-4697-9941-76c36292b841","added_by":"auto","created_at":"2025-08-02 12:11:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912932,"visible":true,"origin":"","legend":"","description":"","filename":"JournalVersion415052025VenkataSubmitted.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6677317/v1_covered_4fbff343-8354-450b-b854-91872af397d6.pdf"}],"financialInterests":"","formattedTitle":"Risk-Informed Life Extension Decisions Using Unstructured Maintenance Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Life Extension, Failure Mode and Effects Analysis, Natural Language Processing, Technical Language Processing, Unstructured Maintenance Data, Risk Priority Number, Aviation Maintenance Analytics","lastPublishedDoi":"10.21203/rs.3.rs-6677317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6677317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Life extension (LE) decision of complex, long-life, capital-intensive assets is a challenge due to availability of structured maintenance data. 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