Rancang Bangun Alat Penganalisa Kerusakan Berdasarkan Getaran dan Kecepatan Putar Motor Menggunakan loT
DOI:
https://doi.org/10.62951/bridge.v4i3.1006Keywords:
Electric Motor, Internet of Things, Machine Learning, Random Forest, VibrationAbstract
Electric motors are important components in various sectors, so monitoring vibration and rotational speed is necessary to maintain optimal performance and minimize downtime. This study aims to design an Internet of Things (IoT)-based motor condition monitoring system by integrating vibration and rotational speed sensors and applying the Random Forest machine learning algorithm to classify motor conditions. The system uses an ESP32 microcontroller, ADXL345 vibration sensor, and TCRT5000 rotational speed sensor. A total of 1,200 datasets representing three fan conditions—normal, loose bushing, and bent shaft—were used to develop the classification model. The Random Forest model uses 15 decision trees with a depth of 5. Evaluation using 240 test data produced 100% accuracy, precision, recall, and F1-score for each condition. The monitoring data were successfully transmitted to a web-based IoT dashboard in real time, with an average update interval of approximately one second.
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