Towards Big Industrial Data Mining Through Explainable Automated Machine Learning

Towards Big Industrial Data Mining Through Explainable Automated Machine Learning | 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 Big Industrial Data Mining Through Explainable Automated Machine Learning Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Mohamed Hamlich, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-755783/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2022 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 3 You are reading this latest preprint version Abstract Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence(AI) models but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameter configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field. Mechanical Engineering Machine learning AutoML Explainable AI Data analysis Decision-support systems Industry 4.0 Full Text Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2022 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviews received at journal 11 Sep, 2021 Reviewers invited by journal 05 Aug, 2021 First submitted to journal 26 Jul, 2021 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. 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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-755783","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":44207836,"identity":"ae36017c-cc89-4326-aefd-408ea0d3d8fe","order_by":0,"name":"Moncef 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