Browsing by Author "2 nd Supervisor: Prof. M.Sc Myagmarjav.B"

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    Study on Vibration-Based Energy Harvesting from Industrial Machinery Vibration for Low-Power Sensor Devices Use
    (GMIT, 2025) Tulga Avarga; 1 st Supervisor: Prof. Ph.D. Odbileg.N; 2 nd Supervisor: Prof. M.Sc Myagmarjav.B
    The growing demand for sustainable and maintenance-free power sources in industrial environments has driven the exploration of ambient energy harvesting technologies. This thesis investigates the use of vibration-based energy harvesting from industrial machinery as a power source for low-power sensor devices. By focusing on piezoelectric transduction, the study presents the design, modeling, and testing of a small-scale energy harvester prototype built around Arduino, piezoelectric elements, and passive power electronics. Field measurements were conducted on vibrating machinery such as motors and pumps operating at 30 Hz and 50 Hz to characterize real-world excitation sources. A cantilever piezoelectric beam made of PZT-5A was designed and analyzed using ANSYS Workbench to determine its natural frequency and harmonic response. The harvester's electrical behavior was simulated using MATLAB/Simulink, and the system's real-time performance was evaluated using a function generator, oscilloscope, and the MPU6050 accelerometer module. The results showed that the harvested voltage ranged from a few hundred millivolts up to ~1.0 V, depending on vibration intensity. The time-domain and frequency-domain Analysis revealed a clear correlation between vibration frequency and harvested voltage. The system reliably activated an LED when the voltage exceeded threshold levels, confirming functionality. Simulation results closely matched experimental data, supporting the validity of the proposed model. This work concludes that piezoelectric vibration energy harvesting is a viable power source solution for autonomous industrial sensor applications. While not yet suitable for With continuous power delivery, the proposed system can effectively support intermittent sensing tasks, reducing battery dependence, and enabling the sustainable deployment of low-power monitoring devices.