Perancangan dan Pembuatan Simulator Pendeteksi Kerusakan Bearing
DOI:
https://doi.org/10.52158/jamere.v6i2.1638Keywords:
bearing fault detection, bearing simulator.Abstract
Wheel bearings are essential mechanical components that support radial and axial loads while ensuring smooth wheel rotation. Early detection of bearing faults is important to improve vehicle safety, reliability, and maintenance efficiency. This study aims to design and develop a wheel bearing fault detection simulator based on a multi-sensor system integrated with an ESP32 microcontroller. An experimental method was employed through the design, fabrication, and integration of mechanical and electronic systems capable of simulating actual bearing operating conditions. The mechanical system consists of a 1-phase AC motor with a maximum speed of 3000 rpm, a pulley-belt transmission, a rotating shaft, and a wheel test assembly. The electronic system integrates an SW-420 vibration sensor, MAX4466 sound sensor, MLX90614 infrared temperature sensor, Hall effect sensor for rotational speed measurement, LCD 20×4 display, and a data logger for real-time monitoring and automatic data recording. The simulator was designed using Autodesk Fusion 360 before being fabricated and assembled into a functional prototype. The developed system successfully integrates all components to simultaneously measure, display, and record vibration, sound, temperature, and rotational speed data during bearing operation. The proposed simulator is expected to provide a reliable experimental platform for future studies on wheel bearing condition monitoring, fault diagnosis, and the development of intelligent predictive maintenance systems
References
[1] H. Wang and J. Xie, “Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments,” 2025.
[2] S. Zhang et al., “Model-Based Analysis and Quantification of Bearing Faults in Induction Machines,” vol. 56, no. 3, pp. 2158–2170, 2020.
[3] M. Electric, “Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review,” 2020.
[4] T. Jalonen, M. Al-sa, S. Kiranyaz, and M. Gabbouj, “Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions”.
[5] N. D. Thuan and H. S. Hong, “HUST bearing : a practical dataset for ball bearing fault diagnosis,” no. 1, pp. 1–29.
[6] B. Peng, Y. Bi, B. Xue, M. Zhang, and S. Wan, “A Survey on Fault Diagnosis of Rolling Bearings,” pp. 1–24, 2022.
[7] B. M. Randhavan and R. Kumar, “Condition Monitoring and Fault Diagnosis of Rolling Contact Element Bearings Based on Artificial Intelligence Techniques : A Review Approach,” vol. 11, no. 6, pp. 2574–2594, 2025.
[8] F. Maesa, “Rancang Bangun Sistem Kontrol dan Monitoring Keamanan Sepeda Motor Berbasis IoT dengan Modul GPS Neo-6M dan Sensor Getar SW-420,” vol. 12, no. 1, pp. 37–43, 2025.
[9] P. Studi, T. Elektro, F. S. Teknologi, and U. B. Darma, “PENERAPAN SENSOR GETAR DAN SENSOR SUHU UNTUK PEMANTAUAN MOTOR DC BERBASIS IOT,” vol. 8, no. 1, pp. 223–232, 2025.
[10] F. A. Syahputra et al., “Penerapan Sistem Pemeliharaan Berbasis Total Productive Maintenance ( TPM ) Pada Automatic Labelling Machine Metica di Industri Kosmetik,” pp. 1878–1887, 2024.
[11] E. Iunusova, M. K. Gonzalez, K. Szipka, A. Archenti, and M. K. Gonzalez, “Early fault diagnosis in rolling element bearings : comparative analysis of a knowledge-based and a data-driven approach,” J. Intell. Manuf., vol. 35, no. 5, pp. 2327–2347, 2024, doi: 10.1007/s10845-023-02151-y.
[12] D. Cornel, F. G. Guzmán, G. Jacobs, and S. Neumann, “Condition monitoring of roller bearings using acoustic emission,” pp. 367–376, 2021.
[13] P. Ong, J. Yin, C. Chee, K. Sia, K. Huong, and L. K. Tung, “Intelligent fault diagnosis of bearings using multi-sensor spectrogram fusion and machine learning models,” Iran J. Comput. Sci., vol. 8, no. 4, pp. 2295–2305, 2025, doi: 10.1007/s42044-025-00316-x.
[14] F. Rahman, Z. Manguluang, M. Tamrin, and Z. Joko, “Analisis Kerusakan Gardan ( Differential ) Dan Pengaruhnya Terhadap Putaran Roda Belakang Pada Kendaraan Fuso Fighter,” no. 29, pp. 40–43, 2018.
[15] A. Setiawan, A. Muid, and I. Nirmala, “Rancang Bangun Alat Pendeteksi Kerusakan Bearing pada Kendaraan Roda Empat Menggunakan Metode KNN ( K- Nearest Neighbor ),” vol. 8, no. 2, pp. 31–38, 2018, doi: 10.26418/positron.v8i2.27508.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sutan Dzaky H.N, Nurhadi

This work is licensed under a Creative Commons Attribution 4.0 International License.









