A Practical Framework for Unsupervised Condition Monitoring and Health Index Tracking of a Linear Accelerator Using Long-Term MPC Data

A Practical Framework for Unsupervised Condition Monitoring and Health Index Tracking of a Linear Accelerator Using Long-Term MPC 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 Article A Practical Framework for Unsupervised Condition Monitoring and Health Index Tracking of a Linear Accelerator Using Long-Term MPC Data Qiang Zhang^, Xiaoxue Wang^, Yubing Liu^, Lei Zhang^, Yajun Liu^, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9296779/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Machine Performance Check (MPC) generates routine quality assurance measurements for linear accelerators, but practical methods for converting long-term MPC records into longitudinal machine-state monitoring remain limited. We retrospectively analyzed 619 MPC records from a Varian VitalBeam linear accelerator collected between July 2021 and January 2026 in a radiotherapy department in China. After leaf-level aggregation and hierarchical missing-value handling, 48 features were retained. A rolling baseline defined by record count was combined with robust scaling, Isolation Forest, exponential weighted moving average smoothing, and dynamic thresholding to derive anomaly scores and a 0–100 health index. Of the 619 observations, 599 yielded valid anomaly scores. Annual anomaly rates were 4.55% in 2021, 0% in 2022, 0.74% in 2023, 0% in 2024, and 4.55% in 2025, with corresponding median health index values of 54.78, 56.61, 56.80, 61.00, and 63.58. Long-term drift was most evident in features related to couch motion, jaw parallelism, and MLC backlash, and deviations were usually shared across multiple subsystems. These findings show that routinely acquired MPC data can support longitudinal machine-state monitoring and may provide a practical basis for predictive quality assurance and equipment health surveillance. Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Machine Performance Check linear accelerator anomaly detection health index machine-state monitoring predictive quality assurance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 01 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. 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-9296779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621925644,"identity":"d9e3516e-2854-4a63-87d1-2678ad8b3413","order_by":0,"name":"Qiang Zhang^","email":"","orcid":"","institution":"Hanzhong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhang^","suffix":""},{"id":621925645,"identity":"a97133e7-5fdf-4d8d-9805-3e0fda382c9a","order_by":1,"name":"Xiaoxue Wang^","email":"","orcid":"","institution":"Hanzhong Central 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