Machine condition monitoring using a predictive maintenance

Azmi, Nurul Izzah (2024) Machine condition monitoring using a predictive maintenance. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Monitoring the condition of machines plays a vital role in guaranteeing the best possible performance and lifespan of industrial equipment. This research proposes a predictive maintenance system for monitoring machine conditions and enhancing industrial equipment performance and longevity. Leveraging the Raspberry Pi, sensors, and Node-RED, the system utilizes various sensors to collect real-time data on motor aspects like temperature and vibration. The Raspberry Pi serves as the central unit for data collection, processing, and analysis. Utilizing Node-RED's visual programming, a comprehensive monitoring and predictive maintenance plan is developed. The system employs advanced data analysis, including machine learning, to identify patterns and anomalies in sensor data, allowing for proactive fault detection aligning with predictive maintenance strategies. The comparison between two motors is analyzed by comparing the vibration and temperature variation. Each motor produces different vibration levels during operation and the differences in the motor’s age, design, and maintenance can affect motor efficiency. The effectiveness of the proposed system is demonstrated through experiments conducted on a real motor setup.

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