{"paper_id":"2f314548-5404-471e-a08f-b00bda3244a7","body_text":"TinyML Anomaly Detection and Fault Prediction for Industrial Applications | 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 TinyML Anomaly Detection and Fault Prediction for Industrial Applications Ernest Toochukwu Samuel, Tommaso Polonelli, Michele Magno, Adedeji Esan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8379288/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 Tiny Machine Learning (TinyML) for industrial applications, in the context of this project, involves the deployment of machine learning (ML) models on the edge for structural health monitoring, to allow low- latency decision-making and proactive preventive maintenance of industrial equipment. Conventional maintenance practices are largely based on routine inspections, such as work-by-inspection (WBI) and scheduled maintenance, which are labor intensive, costly, and susceptible to diagnostic errors. This proactive approach bridges these gaps by detecting and diagnosing potential failures in structures such as bearings, bridges, pipelines, and manufacturing equipment before they result in a catastrophic failure. Recent developments in condition monitoring employ cloud-based ML algorithms; however, these ap- proaches are constrained by high latency, high operational cost, significant energy consumption, and network limitations, particularly in remote industrial environments. In this project, using cement mill bearings as a case study, TinyML was investigated as an alternative solution. A lightweight Temporal Convolutional Network (TCN) was compared to other light weight models and was implemented to predict the bearing temperature of the slide shoe 45 minutes in advance. With a mean absolute error of 0.02048, the approach demonstrated early fault prediction and automated preventive actions through the water sprayer and alarm activation. Artificial Intelligence and Machine Learning Industrial Engineering Electrical Engineering Mechanical Engineering Structural Health Monitoring(SHM) Edge Computing TinyML Industrial IoT Preventive Maintenance Predictive Maintenance Full Text Additional Declarations The authors declare no competing interests. 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. 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Conventional maintenance practices are largely based on routine inspections, such as work-by-inspection (WBI) and scheduled maintenance, which are labor intensive, costly, and susceptible to diagnostic errors. This proactive approach bridges these gaps by detecting and diagnosing potential failures in structures such as bearings, bridges, pipelines, and manufacturing equipment before they result in a catastrophic failure. Recent developments in condition monitoring employ cloud-based ML algorithms; however, these ap- proaches are constrained by high latency, high operational cost, significant energy consumption, and network limitations, particularly in remote industrial environments.\\u003c/p\\u003e \\u003cp\\u003eIn this project, using cement mill bearings as a case study, TinyML was investigated as an alternative solution. A lightweight Temporal Convolutional Network (TCN) was compared to other light weight models and was implemented to predict the bearing temperature of the slide shoe 45 minutes in advance. With a mean absolute error of 0.02048, the approach demonstrated early fault prediction and automated preventive actions through the water sprayer and alarm activation.\\u003c/p\\u003e\",\"manuscriptTitle\":\"TinyML Anomaly Detection and Fault Prediction for Industrial Applications\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-18 04:47:44\",\"doi\":\"10.21203/rs.3.rs-8379288/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"e82a8246-2adc-4330-9093-d7c8a98addab\",\"owner\":[],\"postedDate\":\"December 18th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":59790491,\"name\":\"Artificial Intelligence and Machine Learning\"},{\"id\":59790492,\"name\":\"Industrial Engineering\"},{\"id\":59790493,\"name\":\"Electrical Engineering\"},{\"id\":59790494,\"name\":\"Mechanical Engineering\"}],\"tags\":[],\"updatedAt\":\"2025-12-18T04:47:44+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-18 04:47:44\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8379288\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8379288\",\"identity\":\"rs-8379288\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}