Towards Autonomous Predictive Maintenance: A Bibliometric Review of Machine Learning Approaches and a Self-Learning Agentic AI Framework

Towards Autonomous Predictive Maintenance: A Bibliometric Review of Machine Learning Approaches and a Self-Learning Agentic AI Framework | 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 Towards Autonomous Predictive Maintenance: A Bibliometric Review of Machine Learning Approaches and a Self-Learning Agentic AI Framework Prashant Steele, Alka Bani Agrawal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8570457/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 In today’s competitive marketplace, minimising downtime, expensive repairs, and increasing operational efficiency have made predictive maintenance (PdM) a critical component of an effective industrial system strategy. Industry 4.0, along with the rising availability of sensor data, has spearheaded the utilisation of machine learning (ML) technology in developing sophisticated solutions for predictive maintenance. By examining numerous scientific papers using the inclusion and exclusion criteria, this work investigates what is known and not known in the domain of the research, as well as key challenges, research trends, and future developments. The analysis also pinpoints the leading authors, institutions, and sources of information, as well as the history of applying ML techniques (such as supervised, unsupervised, and deep learning) for predictive maintenance. In addition, the paper addresses challenges and opportunities as well as possibilities of implementing machine learning based predictive maintenance systems in real conditions, such as data representativeness, transparency of machine learning models, and deployment in a real-time environment. This work meticulously presents a systematic bibliometric analysis of the intersection between predictive maintenance and industrial machines using machine learning algorithms. This review proposed a novel self-learning agent-based predictive maintenance framework capable of autonomy to make decisions with minimum human assistance involving orchestrations of maintenance AI agents, thus taking a step towards a fully adaptive and intelligent industrial system. Machine Learning (ML) Industrial Machines Intelligent Maintenance Agent-Based Systems Autonomous Systems Full Text Additional Declarations No competing interests reported. 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. 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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-8570457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586983256,"identity":"a558a2e2-08d8-426e-ae6b-c50cb35196ad","order_by":0,"name":"Prashant Steele","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYJACZjjrAYMNnM1DSIsEAxuQTGBII13LYcKOMpc+/PhzQc2dOvn5zQcfJFScz5eP7jFg+FHDIGOOQ4tlX5qZ9IxjzyQMjrElGyScuW258c4ZA8aeYww8lg3YtRicYTBj5mE7LGHAxmMmkdh228BwRloCA28DA4/BAVxa2D9/5vl3WEK+jf/7j8R/58BaGP/i1cJjIM3bdliC4RgPG0NiwwEDeYnkA8z4bLHs4SmT5u07LLnhWJqxRMKxZAMDmcMHDssck8CpxZyHffNnnm+H+eWbDz/88KHGzkB+dmPjwzc1NvY4HYYpcoOB4QAoonABTC3yM3AqHgWjYBSMghEKAKtvVzKBUT5hAAAAAElFTkSuQmCC","orcid":"","institution":"University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya","correspondingAuthor":true,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Steele","suffix":""},{"id":586983257,"identity":"950817b8-0562-4563-8e30-ce327f1ae9b2","order_by":1,"name":"Alka Bani Agrawal","email":"","orcid":"","institution":"University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Alka","middleName":"Bani","lastName":"Agrawal","suffix":""}],"badges":[],"createdAt":"2026-01-10 21:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8570457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8570457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297338,"identity":"826e34d8-f445-4707-8039-a7c264494ae4","added_by":"auto","created_at":"2026-02-10 10:27:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1621612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptfinalok.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8570457/v1_covered_7c15b987-d81e-46df-a5f5-2764a66467f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards Autonomous Predictive Maintenance: A Bibliometric Review of Machine Learning Approaches and a Self-Learning Agentic AI Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Machine Learning (ML), Industrial Machines, Intelligent Maintenance, Agent-Based Systems, Autonomous Systems","lastPublishedDoi":"10.21203/rs.3.rs-8570457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8570457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn today\u0026rsquo;s competitive marketplace, minimising downtime, expensive repairs, and increasing operational efficiency have made predictive maintenance (PdM) a critical component of an effective industrial system strategy. 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