Early Detection of Ball Bearing Faults Using the Decision Tree Method
Abstract
Bearings are one of the important components in the machine that functions as a holder and positions the shaft alignment radially when rotating. Statistics show that about 50% of failures in electric motors are related to bearings. Therefore, monitoring bearing performance and efficiency before damage occurs is necessary to avoid more serious damage and save repair costs. This research aims to build a classification model that can identify bearings in normal condition and 6 types of damage (inner crack, outer crack, ball crack, and a combination of both) using the HUST dataset. The model building process begins with collecting datasets, processing and extracting dataset features, building classification models and evaluating the models that have been made. A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization. The results of the decision tree model that has been built are able to identify bearing damage with an accuracy of 94.47%.
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References
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