IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT
Smart 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...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2023-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v5-n1/6.pdf |
| _version_ | 1797870131965067264 |
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| author | Rajiv Kumar Sharma |
| author_facet | Rajiv Kumar Sharma |
| author_sort | Rajiv Kumar Sharma |
| collection | DOAJ |
| description | Smart 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 framework for 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. |
| first_indexed | 2024-04-10T00:22:30Z |
| format | Article |
| id | doaj.art-c3fd31611bb549cdbf7a864f76fcea6f |
| institution | Directory Open Access Journal |
| issn | 2620-2832 2683-4111 |
| language | English |
| last_indexed | 2024-04-10T00:22:30Z |
| publishDate | 2023-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj.art-c3fd31611bb549cdbf7a864f76fcea6f2023-03-15T16:59:13ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112023-03-0151637210.24874/PES05.01.006IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENTRajiv Kumar Sharma 0https://orcid.org/0000-0002-4018-5869National Institute of Technology, Department of Mechanical Engineering NIT Hamirpur-H.P, IndiaSmart 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 framework for 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.https://pesjournal.net/journal/v5-n1/6.pdfpredictive maintenancemachine learningcondition monitoringtool wear |
| spellingShingle | Rajiv Kumar Sharma IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT Proceedings on Engineering Sciences predictive maintenance machine learning condition monitoring tool wear |
| title | IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT |
| title_full | IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT |
| title_fullStr | IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT |
| title_full_unstemmed | IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT |
| title_short | IMPROVING QUALITY OF PREDICTIVE MAINTENANCE THROUGH MACHINE LEARNING ALGORITHMS IN INDUSTRY 4.0 ENVIRONMENT |
| title_sort | improving quality of predictive maintenance through machine learning algorithms in industry 4 0 environment |
| topic | predictive maintenance machine learning condition monitoring tool wear |
| url | https://pesjournal.net/journal/v5-n1/6.pdf |
| work_keys_str_mv | AT rajivkumarsharma improvingqualityofpredictivemaintenancethroughmachinelearningalgorithmsinindustry40environment |