Resilient Edge-Historian Framework for Industrial Automation: Adaptive Multi-Trend HMI under Intermittent Network Conditions

Resilient Edge-Historian Framework for Industrial Automation: Adaptive Multi-Trend HMI under Intermittent Network Conditions | 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 Resilient Edge-Historian Framework for Industrial Automation: Adaptive Multi-Trend HMI under Intermittent Network Conditions Muhammed Ali Erbir, Fatma Gul Amil, Huseyin Burak Akyol, Mahmut Altun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327528/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 24 You are reading this latest preprint version Abstract Continuous recording of process data in production automation and industrial control applications is critical for fault analysis, quality monitoring, maintenance planning, and operational reporting. However, field environments often require systems to operate under constrained network conditions, such as limited bandwidth, high latency, or even intermittent connectivity. This study presents an edge-based time-series data historian and a connection-aware HMI trend visualization approach designed to operate reliably under such intermittent connectivity conditions. In the proposed architecture, the control layer runs on a TwinCAT-based industrial computer, where process variables are periodically collected and written to a time-series database deployed at the edge through a flow-based integration layer. On the HMI side, multiple trend charts can be visualized simultaneously. Each chart supports dynamic queries through selectable variable and time-range menus. Experimental results show that for queries retrieving 180 data points over a 24-hour period, the P95 latency is approximately 1920 ms over VPN and approximately 355 ms over LAN. With adaptive refresh mechanisms, the risk of interface timeouts is reduced. The contributions of this work include: (i) a connection-aware refresh policy that adapts the refresh period according to LAN/VPN network conditions; (ii) dynamic downsampling that produces a fixed number of values (e.g., 180) regardless of the selected time range, thereby bounding query cost; and (iii) an operational continuity mechanism that ensures automatic service recovery after power interruptions or system restarts. The proposed system has been deployed on a real industrial field automation infrastructure, and measurements were collected during live operation using actual process data. The paper presents the system architecture, data model, and a reproducible experimental evaluation methodology. Also, it reports performance results based on metrics such as writing latency, query latency, and recovery time. Industrial IoT Edge Computing Time-Series Database Historian HMI Trend Analysis 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 17 May, 2026 Reviews received at journal 14 May, 2026 Reviews received at journal 13 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 07 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed 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 agreed at journal 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 21 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 05 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. 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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-9327528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630050308,"identity":"9a0fc111-2e7f-43a8-91bb-e26d1e34eaca","order_by":0,"name":"Muhammed Ali 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