Vibration-Based Predictive Maintenance System for Industrial Machinery
DOI:
https://doi.org/10.15662/IJEETR.2026.0802255Keywords:
Predictive maintenance, vibration analysis, condition monitoring, fault diagnosis, industrial machinery, signal processing, machine learning, sensor systems, anomaly detection, maintenance optimizationAbstract
Predictive maintenance has become a crucial strategy in modern industries to reduce unexpected machine failures and maintenance costs. This paper presents a vibration-based predictive maintenance system designed to monitor machine health in real time. The system utilizes vibration sensors to capture machine vibration signals, which are processed using a microcontroller for fault detection. Variations in vibration patterns indicate potential issues such as imbalance, misalignment, or bearing faults. The collected data is analyzed to identify abnormal conditions and alert users before failure occurs. The proposed system improves operational efficiency, minimizes downtime, and enhances equipment lifespan. Experimental results demonstrate that the system effectively detects faults at early stages, making it a reliable and cost-effective solution for industrial applications
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