Development of a Real-Time Sensor-Based Digital Twin for Predictive Maintenance of Vehicle Gearbox Systems
DOI:
https://doi.org/10.55927/ijsmr.v4i7.93Keywords:
Digital Twin, Real-Time Sensor, Predictive Maintenance, Vehicle Gearbox, Condition MonitoringAbstract
This study develops a real-time sensor-based digital twin for predictive maintenance of vehicle gearbox systems. The contribution lies in integrating vibration sensing, IoT-based data transmission, and machine learning to monitor gearbox health and detect anomalies early. A case study was conducted on a tactical vehicle gearbox using three ADXL345 accelerometers installed at the input shaft, gear mesh, and output shaft. Vibration data were collected in real time under three operating conditions over multiple observation days. The proposed model used Random Forest for condition classification and GRU neural network for health scoring. Results show that the system can identify gearbox conditions accurately and support earlier maintenance decisions, offering a practical basis for condition-based maintenance.
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