Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques
This study aims to develop a predictive maintenance system for an aging vertical grinding machine, operational since 1978, by integrating machine learning techniques, vibration analysis, and fuzzy logic. The research addresses the challenges of increased wear and unexpected failures in older machin...
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2025-06-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | http://journal.yrpipku.com/index.php/jaets/article/view/6417 |
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| author | Primawati Primawati Fitrah Qalbina Mulyanti Mulyanti Ferra Yanuar Dodi Devianto Remon Lapisa Fazrol Rozi |
| author_facet | Primawati Primawati Fitrah Qalbina Mulyanti Mulyanti Ferra Yanuar Dodi Devianto Remon Lapisa Fazrol Rozi |
| author_sort | Primawati Primawati |
| collection | DOAJ |
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This study aims to develop a predictive maintenance system for an aging vertical grinding machine, operational since 1978, by integrating machine learning techniques, vibration analysis, and fuzzy logic. The research addresses the challenges of increased wear and unexpected failures in older machinery, which can lead to costly downtime and reduced operational efficiency. Vibration and temperature data were collected over 12 days using an MPU-9250 accelerometer, with conditions categorized as good, fair, and faulty. Various machine learning models, including logistic regression, k-nearest neighbors, support vector machines, decision trees, random forest, and Naive Bayes, were trained to classify bearing states. The random forest model achieved the highest accuracy of 94.59%, demonstrating its effectiveness in predicting machine failures. The results highlight the potential of combining multi-dimensional sensor data with advanced analytics to enable early fault detection, minimize downtime, and improve operational efficiency. This approach provides a cost-effective solution for maintaining aging machinery and contributes to both theoretical advancements in machine learning applications and practical improvements in industrial maintenance practices. The study’s findings offer scalable insights for industries reliant on legacy equipment, promoting sustainable manufacturing through optimized resource use and enhanced reliability.
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| format | Article |
| id | doaj-art-7ffa00e12f9e4834a3071b6ea7e4ec6a |
| institution | DOAJ |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-7ffa00e12f9e4834a3071b6ea7e4ec6a2025-08-20T03:10:41ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792025-06-016210.37385/jaets.v6i2.6417Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques Primawati Primawati0Fitrah Qalbina1Mulyanti Mulyanti2Ferra Yanuar3Dodi Devianto4Remon Lapisa5Fazrol Rozi6Universitas Negeri PadangUniversitas Negeri PadangUniversitas Negeri PadangUniversitas AndalasUniversitas AndalasUniversitas Negeri PadangPoliteknik Negeri Padang This study aims to develop a predictive maintenance system for an aging vertical grinding machine, operational since 1978, by integrating machine learning techniques, vibration analysis, and fuzzy logic. The research addresses the challenges of increased wear and unexpected failures in older machinery, which can lead to costly downtime and reduced operational efficiency. Vibration and temperature data were collected over 12 days using an MPU-9250 accelerometer, with conditions categorized as good, fair, and faulty. Various machine learning models, including logistic regression, k-nearest neighbors, support vector machines, decision trees, random forest, and Naive Bayes, were trained to classify bearing states. The random forest model achieved the highest accuracy of 94.59%, demonstrating its effectiveness in predicting machine failures. The results highlight the potential of combining multi-dimensional sensor data with advanced analytics to enable early fault detection, minimize downtime, and improve operational efficiency. This approach provides a cost-effective solution for maintaining aging machinery and contributes to both theoretical advancements in machine learning applications and practical improvements in industrial maintenance practices. The study’s findings offer scalable insights for industries reliant on legacy equipment, promoting sustainable manufacturing through optimized resource use and enhanced reliability. http://journal.yrpipku.com/index.php/jaets/article/view/6417Predictive Maintenance SystemAging Vertical Grinding MachineVibrationMachine LearningFuzzy Logic |
| spellingShingle | Primawati Primawati Fitrah Qalbina Mulyanti Mulyanti Ferra Yanuar Dodi Devianto Remon Lapisa Fazrol Rozi Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques Journal of Applied Engineering and Technological Science Predictive Maintenance System Aging Vertical Grinding Machine Vibration Machine Learning Fuzzy Logic |
| title | Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques |
| title_full | Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques |
| title_fullStr | Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques |
| title_full_unstemmed | Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques |
| title_short | Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques |
| title_sort | predictive maintenance of old grinding machines using machine learning techniques |
| topic | Predictive Maintenance System Aging Vertical Grinding Machine Vibration Machine Learning Fuzzy Logic |
| url | http://journal.yrpipku.com/index.php/jaets/article/view/6417 |
| work_keys_str_mv | AT primawatiprimawati predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT fitrahqalbina predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT mulyantimulyanti predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT ferrayanuar predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT dodidevianto predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT remonlapisa predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques AT fazrolrozi predictivemaintenanceofoldgrindingmachinesusingmachinelearningtechniques |