<![CDATA[The development of Industry 4.0 has shifted maintenance strategies from corrective maintenance to predictive maintenance based on machine condition monitoring. Vibration analysis is widely used to detect machine failures before unexpected breakdowns occur. However, limited learning media in predictive maintenance courses reduce students' practical understanding of bearing fault characteristics. This study aims to design a vibration-based bearing fault simulation learning media to support practical learning activities. The research methods include data collection, tool design, manufacturing, assembly, and testing. The developed system consists of an AC motor, shaft, coupling, bearing housing, speed controller, and bearings with several fault conditions. The results indicate that the system can simulate inner race, outer race, and rolling element defects, producing different vibration characteristics. Therefore, the proposed learning media can improve students' understanding of predictive maintenance and vibration analysis in a practical manner.]]>
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