[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127506-en":3,"doc-seo-127506-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127506,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT - Paper","Smart manufacturing leverages Industry 4.0 enablers to support decision making and resource planning using operational data. Digitalization and industrial machine connectivity enable real-time acquisition from multiple sensors such as current, acoustic, and vibration during ongoing processes. The paper proposes a PdM 4.0 framework and validates it through a milling manufacturing case, using a public NASA dataset to build and test the system. Machine learning regressors including SVR, RF, DT, XGBoost, and MLP predict remaining useful life and tool wear rate, with model quality assessed via R-square, RMSE, and MAE.","Vol. 05, No. 1 (2023) 63-72, doi: 10.24874/PES05.01.006  \nProceedings on Engineering Sciences  \n[www.pesjournal.net](www.pesjournal.net)  \nIMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT  \nRajiv Kumar Sharma 1  \nReceived 21.10.2022.  \nAccepted 18.01.2023. UDC – 338.364:[005.934.2:004.85]  \nKeywords:  \nPredictive maintenance, Machine learning, Condition monitoring, Tool wear, Remaining useful life  \nA B S T R A C T  \nSmart manufacturing is the modern form of manufacturing that utilizes Industry 4.0 enablers for decision making and resources planning by taking advantage of the available data. With the advancement of digitalization and industrial machine connectivity, it is now feasible to gather data in real-time from a variety of sensors (e.g. current, acoustic, vibration etc.) while the process is being carried out. The aim of the paper is to propose a frameworkfor predictive maintenance PdM 4.0 and validate the framework by implementing it for a manufacturing process, milling in which a public data set from NASA repository is used to build and test the proposed PdM 4.0 system. The various machine learning classifiers such as: support vector regression SVR, RF, DT, XGBoost and MLP regressor have been used for remaining useful life and tool wear rate prediction. The model evaluation and comparison is based on metrics like (R-square), root mean square error and mean absolute error.  \n© 2023 Published by Faculty of Engineering  \n1. INTRODUCTION  \nToday’s manufacturing organisations are under pressure to be more flexible, reduce downtime and costs and increase efficiencies. In addition to making new investments in production and technology, data-driven manufacturing companies are responding to these pressures by leveraging the capabilities of artificial intelligence (AI), the industrial internet of things, (IIoT), cloud computing technologies and innovations in smart measurement and quality data management systems—resulting in greater visibility into their operations. Today, poor maintenance strategies can reduce a plant’s overall productive capacity by 5 to 20 % . Recent studies also show that unplanned downtime is costing industrial manufacturers an estimated $50 billion each year.This begs the question,“How often should a machine be taken  \noffline to be serviced?” Traditionally, this dilemma forced most organizations into a trade-off situation where they had to choose between maximizing the useful life of a part at the risk of machine downtime (run-to-failure) or attempt to maximize uptime through early replacement of potentially good parts (time-based preventive maintenance), which has been demonstrated to be ineffective for most equipment components. Artificial Intelligence (AI) is already transforming manufacturing by outperforming humans in its ability to provide insights that inform timely, data-driven decisions and productivity improvements. In some operations, it looks for conditions like idle equipment or scheduled maintenance in order to make decisions about reassigning parts measurement.  \nIndustry 4.0 which is focused on the interconnectivity of the system through the digitalization of industry, the amount of data generated by the sensors is enormous and there is a lot of information which can be gathered after applying proper techniques. Industry 4.0 comprises of two sections, the front-end technologies address four dimensions: smart manufacturing, smart products, smart supply chain, and smart working, whereas base technologies consider four elements: Internet of Things, cloud services, big data, and analytics are all buzzwords these days (Frank, Dalenogare, & Ayala, 2019) . It contains a wide scope of processes, systems and technologies that are primarily relevant to industry's digitalization. The four technologies that are related to the data and it’s processing comprises of Industrial Internet of Things (IIoT), Cyber Physical Systems (CPS), Cloud Soluti","cbCaimXTPHLP1SzT","https://ap.wps.com/l/cbCaimXTPHLP1SzT","pdf",607854,1,10,"English","en",105,"# Introduction\n## Industry 4.0 and data-driven manufacturing\n## Predictive maintenance and machine learning approaches","[{\"question\":\"What problem does predictive maintenance address in manufacturing?\",\"answer\":\"Predictive maintenance targets the decision of when to service equipment to reduce downtime and costs while improving efficiency, avoiding ineffective time-based preventive maintenance and the risks of run-to-failure strategies.\"},{\"question\":\"What dataset and manufacturing process are used to validate the proposed PdM 4.0 framework?\",\"answer\":\"The framework is validated using a milling manufacturing process, employing a public data set from the NASA repository to build and test the PdM 4.0 system.\"},{\"question\":\"Which machine learning models are used for remaining useful life and tool wear prediction?\",\"answer\":\"The study uses regression/classification learners including support vector regression (SVR), random forest (RF), decision tree (DT), XGBoost, and MLP regressor.\"}]","IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT - 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