{"paper_id":"48ee09a6-e09e-4276-860b-6b7b746fcc69","body_text":"IGBT Remaining Useful Life Prediction Using Optimized ELM | 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 IGBT Remaining Useful Life Prediction Using Optimized ELM Mohanad Mohammed, Ibraheem Abdullah Mohammed Shayea This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9385956/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract To achieve accurate remaining useful life (RUL) estimation of Insulated Gate Bipolar Transistors (IGBTs), this study presents an enhanced Extreme Learning Machine (ELM) model optimized using an improved Nutcracker Optimization Algorithm with mixed strategies (NOAM). Experimental data obtained from NASA’s accelerated aging tests of IGBTs are employed, where the peak collector–emitter voltage is identified as the key degradation indicator for failure characterization. Traditional NOAM suffers from premature convergence during the early optimization stage and insufficient precision in later iterations. To overcome these limitations, the proposed approach increases population diversity by initializing candidate solutions using a Bernoulli mapping sequence, thereby strengthening global exploration. In addition, a novel adaptive weight calculation scheme is introduced to improve local search performance, while Cauchy mutation is incorporated to further enhance solution refinement and convergence accuracy. The optimized NOAM is then applied to tune the parameters of the ELM model, resulting in improved prediction performance. Comparative studies with conventional ELM, PSO-ELM, and SSA-ELM models, along with ablation analyses of the proposed improvement mechanisms, demonstrate that the NOAM-ELM approach achieves superior accuracy in IGBT remaining life prediction. These results indicate that the proposed method provides an effective reference framework for RUL prediction of IGBT devices. Physical sciences/Engineering Physical sciences/Mathematics and computing Remaining useful life prediction IGBT aging analysis Nutcracker optimization algorithm chaos-based initialization metaheuristic optimization extreme learning machine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 15 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 11 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9385956\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":629657777,\"identity\":\"992977e3-9ff9-448a-8d71-e82aae6d1cfb\",\"order_by\":0,\"name\":\"Mohanad Mohammed\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYFACHjDJ2M/efOAAmHmAWC0ze44lQPQcYGBsIErLhhs5BgxEadFt7z346WaOjWzDgZyPhz+2Mcjx3Uhgf1yBR4vZmXPJ0rnb0owbG85uOHCwjcFY8kYCY+MZfFqA7gFqOZzYzNgL1pK4AaQFn8uAWox/5277n9jGzPMApKWeGC1mQFsOJPaw8TCAtCQYENRy5oyZde62ZOMZPGwGB86ckzCceeZh40y8Wo73GN/O3WYnu//+48cfKsps5PmOJx/4iE8LOpAAYkIxOQpGwSgYBaOAIAAAN+xd+MLXzBIAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Istanbul Technical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mohanad\",\"middleName\":\"\",\"lastName\":\"Mohammed\",\"suffix\":\"\"},{\"id\":629657778,\"identity\":\"e8d00cc7-d16d-42e2-8e5c-2f219e45718c\",\"order_by\":1,\"name\":\"Ibraheem Abdullah Mohammed Shayea\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Istanbul Technical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ibraheem\",\"middleName\":\"Abdullah Mohammed\",\"lastName\":\"Shayea\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-11 08:23:52\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9385956/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9385956/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108182964,\"identity\":\"0616dbb0-7842-4584-9788-45e9218b78f0\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 08:59:43\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":488380,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"IGBTSpringerNature.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9385956/v1_covered_66a6362b-f3fa-4b5d-a0c4-c88032a247a0.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"IGBT Remaining Useful Life Prediction Using Optimized ELM\",\"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\":\"info@researchsquare.com\",\"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\":\"Remaining useful life prediction, IGBT aging analysis, Nutcracker optimization algorithm, chaos-based initialization, metaheuristic optimization, extreme learning machine\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9385956/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9385956/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eTo achieve accurate remaining useful life (RUL) estimation of Insulated Gate Bipolar Transistors (IGBTs), this study presents an enhanced Extreme Learning Machine (ELM) model optimized using an improved Nutcracker Optimization Algorithm with mixed strategies (NOAM). Experimental data obtained from NASA\\u0026rsquo;s accelerated aging tests of IGBTs are employed, where the peak collector\\u0026ndash;emitter voltage is identified as the key degradation indicator for failure characterization. Traditional NOAM suffers from premature convergence during the early optimization stage and insufficient precision in later iterations. To overcome these limitations, the proposed approach increases population diversity by initializing candidate solutions using a Bernoulli mapping sequence, thereby strengthening global exploration. In addition, a novel adaptive weight calculation scheme is introduced to improve local search performance, while Cauchy mutation is incorporated to further enhance solution refinement and convergence accuracy. The optimized NOAM is then applied to tune the parameters of the ELM model, resulting in improved prediction performance. Comparative studies with conventional ELM, PSO-ELM, and SSA-ELM models, along with ablation analyses of the proposed improvement mechanisms, demonstrate that the NOAM-ELM approach achieves superior accuracy in IGBT remaining life prediction. 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