[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127324-en":3,"doc-seo-127324-105":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},127324,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Prediction of remaining useful life and downtime of induction motors with supervised machine learning","Research develops a supervised machine learning approach for predictive maintenance of three-phase induction motors in manufacturing by using vibration monitoring. Accelerometer sensors collect performance-related parameters, followed by data preprocessing to address missing values, choose predictor attributes, and remove duplicates. Decision Tree, Naive Bayes, Random Forest, and Artificial Neural Network models are trained for downtime classification and Remaining Useful Life (RUL) estimation. Decision Tree and Naive Bayes achieve perfect downtime classification performance, while Random Forest shows superior RUL prediction errors and correlation metrics.","Submitted: 2025-02-15 | Revised: 2025-05-18 | Accepted: 2025-06-01  \nCC-BY 4.0  \nKeywords: downtime, induction motor, machine learning, predictive maintenance, remaining useful le  \nMuhammad Dzulfiqar ANINDHITO 1 , SUHARJITO 1*  \n1 BINUS University, Indonesia, [muhammad.anindhito002@binus.ac.id](muhammad.anindhito002@binus.ac.id), [suharjito@binus.edu](suharjito@binus.edu)[ ](suharjito@binus.edu)* [Corresponding author: muhammad.anindhito002@binus.ac.id](Corresponding author: muhammad.anindhito002@binus.ac.id)  \nPrediction of remaining useful life and downtime of induction motors with supervised machine learning  \nAbstract  \nThis research aims to use a vibration monitoring system along with machine learning techniques to predict the downtime and Remaining Useful Life (RUL) of three-phase induction motors in the manufacturing sector. The study obtains measurement data from accelerometer sensors that collect various parameters related to motor performance. The research includes a datapreprocessing stage to handle missing data, select predictor attributes, and remove duplicates. Supervised learning algorithms are applied, including Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Artificial Neural Network (ANN). The results show that DT and NB models have the best performance in downtime classification, achieving 100% accuracy, recall, precision and F1 values. In terms of predicting Remaining Useful Life (RUL), the RF model outperforms the base model and ANN, showing better results in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and correlation coefficient.  \n1. INTRODUCTION  \nInduction motors are widely used in modern manufacturing, accounting for approximately 40% of electrical energy consumption and representing a critical component of production systems. The most commonly used induction motors are three-phase induction motors, which operate in three-phase power systems and are widely used in various industrial applications due to their large capacity (Sengamalai et al., 2022).These motors operate on the principle of a rotating magnetic field generated by a three-phase electric current flowing through the stator winding. Despite their reputation for durability, induction motors are susceptible to degradation and damage from factors such as mechanical wear, unbalance, and electrical problems. Common types of damage include mechanical imbalance, bearing failure and rotor damage. Therefore, vibration measurement is used asa predictive maintenance approach to check the condition of the motor (Malta et al., 2014) .  \nIn the case study conducted at a company in the Fast Moving Consumer Goods sector, the use of 3-phase induction motors as drivers for production machines is of great importance. The reason for evaluating the induction motors is that the number of failures has increased significantly over the past two years. An effective way to detect and predict damage to induction motors is to monitor the vibrations generated during operation. Abnormal vibrations are often an early sign of problems such as misalignment, worn bearings or rotor imbalance. The problem in this case study is that although vibration monitoring has been performed on the motor, damage to the induction motor is difficult to detect, resulting in frequent downtime. This is due to inaccurate analysis of vibration monitoring results by technicians, so a method of automation using machine learning is needed so that vibration monitoring can be analyzed and evaluated with computational support and accurate predictions can be made.  \nMany studies have shown that machine learning algorithms can be used to classify types of damage and predict the remaining life of machines with an accuracy of more than 90% . Vibration monitoring systems on rotating machinery can be used to diagnose faults and have a positive impact on making more accurate maintenance decisions (Tiboni et al., 2022) . The remaining life of ","cbCaia4GVmuy5zWm","https://ap.wps.com/l/cbCaia4GVmuy5zWm","pdf",891535,1,15,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n## Vibration as a Condition Indicator\n## Maintenance and Fault Diagnosis with Vibration","[{\"question\":\"What data source and signals are used to predict downtime and RUL for induction motors?\",\"answer\":\"The study uses vibration monitoring with accelerometer sensors to collect parameters related to induction motor performance during operation.\"},{\"question\":\"Which supervised learning models are evaluated in the research?\",\"answer\":\"Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Artificial Neural Network (ANN) are applied for downtime classification and RUL prediction.\"},{\"question\":\"How do the models perform for downtime classification and RUL prediction?\",\"answer\":\"DT and NB provide the best downtime classification results with 100% accuracy and related metrics. For RUL, RF outperforms the baseline and ANN, achieving lower RMSE/MAE/MAPE and a better correlation coefficient.\"}]","Prediction of remaining useful life and downtime of induction motors with supervised machine learning | PDF",1785938294,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-of-remaining-useful-life-and-downtime-of-induction-motors-with-supervised-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/prediction-of-remaining-useful-life-and-downtime-of-induction-motors-with-supervised-machine-learning/127324/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data source and signals are used to predict downtime and RUL for induction motors?","Question",{"text":76,"@type":77},"The study uses vibration monitoring with accelerometer sensors to collect parameters related to induction motor performance during operation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which supervised learning models are evaluated in the research?",{"text":81,"@type":77},"Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Artificial Neural Network (ANN) are applied for downtime classification and RUL prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the models perform for downtime classification and RUL prediction?",{"text":85,"@type":77},"DT and NB provide the best downtime classification results with 100% accuracy and related metrics. 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