[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121454-en":3,"doc-seo-121454-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121454,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Utilizing Machine Learning for Predictive Maintenance of Production Machinery in Small and Medium Enterprises - Deployable Monitoring Approach","Predictive maintenance focuses on early detection of potential machine failures and timely maintenance to prevent breakdowns in small and medium enterprises. Machine learning provides the statistical modeling capability to learn from sensor data collected via microcontrollers, including vibration, acceleration, temperature, and sound. After cleaning, standardizing, and splitting data into 70% training and 30% testing, K-nearest neighbor, decision tree, and random forest models are tuned and evaluated using accuracy, precision, recall, and F1-score. The random forest model achieves strong performance (F1-score 91.22%) and is deployed in a monitoring system for real-time failure prediction.","Utilizing machine learning for predictive maintenance of production machinery in small and medium enterprises  \nYopa Eka Prawatya*, Noveicalistus H. Djanggu, Ratih Rahmahwati, Steven Lourensius  \nIndustrial Engineering Department, Universitas Tanjungpura, Jalan Prof. Dr. H. Hadari Nawawi, Pontianak, Kalimantan Barat, Indonesia 78124  \n*[Corresponding Author](Corresponding Author: yopa.prawatya@industrial.untan.ac.id)[: ](Corresponding Author: yopa.prawatya@industrial.untan.ac.id)[yopa.prawatya@industrial.untan.ac.id](Corresponding Author: yopa.prawatya@industrial.untan.ac.id)  \n\n| Article history: |  | ABSTRACT |\n| --- | --- | --- |\n| Received: 18 September 2024\u003Cbr>Revised: 23 June 2025\u003Cbr>Accepted: 26 June 2025\u003Cbr>Published: 30 June 2025\u003Cbr>Keywords:\u003Cbr>Predictive maintenance Machine learning\u003Cbr>K-nearest neighbor Decision tree Random forest |  | Predictive maintenance involves the early detection of potential machine failures and subsequent maintenance to prevent such failures. Machine learning is a pertinent statistical method for predictive maintenance, enabling the early detection of machine failures and the implementation of preventive measures through a model. The development of the machine learning model commences with data collection from the machine, encompassing vibration, acceleration, machine temperature, and machine sound, facilitated by a microcontroller equipped with sensors. Subsequently, the data undergoes cleaning, including removing outliers or missing values and standardization. Data is partitioned into 70% allocated for training and 30% for testing. After determining hyperparameters and their values through hyperparameter tuning, the training data is utilized to train machine learning models, such as K-nearest neighbor, decision tree, and random forest models. Post-training, the models are evaluated using the remaining test data, employing performance metrics such as accuracy, precision, recall, and F1-score. The random forest model excels due to its utilization of a substantial number of trees for predictions and the full exploitation of the variables which F1-score is 91.22% . The best-performing model is subsequently deployed into a monitoring system, providing real-time machine condition predictions. The deployment results validate the accurate prediction of machine failures. |\n| DOI:\u003Cbr>[https://doi.org/10.31315/opsi.v18i1.13479](https://doi.org/10.31315/opsi.v18i1.13479) | This is an open access article under the CC–BY license.\u003Cbr> |  |\n\n1. INTRODUCTION  \nThe advancement of the manufacturing sector is heavily reliant on the utilization of diverse machinery and equipment, aimed at bolstering efficiency, curbing costs, and expediting operational workflows. As such, the maintenance of these assets assumes critical significance. The challenges associated with maintenance management are not confined to large enterprises but also impinge on conventional businesses that may lack a comprehensive grasp of maintenance protocols. Beyond the dearth of maintenance comprehension, thereactive nature of maintenance exacerbates issues, with equipment often being serviced solely upon malfunction. This approach precipitates various ramifications including diminished output quality,  \nprocessing velocity, and safety standards as equipment nears breakdown. Regular maintenance serves to sustain the optimal condition of machinery. Predictive maintenance stands out as an efficacious strategy toavert breakdowns. Grounded in prognostication tools leveraging historical data, statistical inference methodologies, and technical paradigms, predictive maintenance facilitates early identification of potential issues [1], [2]. In this regard, machine learning offers a powerful computational approach for processing large volumes of sensor and operational data commonly found in industrial settings. Through its ability to uncover complex patterns and adapt to new data, machine learning plays a critical role in solving various maintenan","cbCaiq1clUvnX1o2","https://ap.wps.com/l/cbCaiq1clUvnX1o2","pdf",821239,1,10,"English","en",105,"# Abstract\n## Introduction\n## Methodology (Data Collection and Preprocessing)\n## Model Training and Hyperparameter Tuning\n## Model Evaluation\n## Deployment and Validation\n## Conclusion","[{\"question\":\"What problem does predictive maintenance address for production machinery in SMEs?\",\"answer\":\"It enables early detection of potential machine failures and supports subsequent maintenance actions to prevent breakdowns, reducing quality loss, slower processing, and safety issues near failure.\"},{\"question\":\"Which sensor signals are used to train the predictive maintenance models?\",\"answer\":\"The approach collects vibration, acceleration, machine temperature, and machine sound using a microcontroller equipped with sensors.\"},{\"question\":\"Why does the random forest model perform best in this study?\",\"answer\":\"It leverages many trees for prediction and fully exploits the available variables, achieving an F1-score of 91.22% and accurate real-time failure predictions after deployment.\"}]","Utilizing Machine Learning for Predictive Maintenance of Production Machinery in Small and Medium Enterprises - Deployable Monitoring Approach | PDF",1785735731,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"utilizing-machine-learning-for-predictive-maintenance-of-production-machinery-in-small-and-medium-enterprises-deployable-monitoring-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/utilizing-machine-learning-for-predictive-maintenance-of-production-machinery-in-small-and-medium-enterprises-deployable-monitoring-approach/121454/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does predictive maintenance address for production machinery in SMEs?","Question",{"text":75,"@type":76},"It enables early detection of potential machine failures and supports subsequent maintenance actions to prevent breakdowns, reducing quality loss, slower processing, and safety issues near failure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which sensor signals are used to train the predictive maintenance models?",{"text":80,"@type":76},"The approach collects vibration, acceleration, machine temperature, and machine sound using a microcontroller equipped with sensors.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the random forest model perform best in this study?",{"text":84,"@type":76},"It leverages many trees for prediction and fully exploits the available variables, achieving an F1-score of 91.22% and accurate real-time failure predictions after deployment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]