Smart Maintenance System (SMAT) : Predictive Maintenance of Electrical Motor Applications
DOI:
https://doi.org/10.56532/mjsat.v5i2.473Keywords:
Predictive maintenance, Machine learning, Electrical motorAbstract
Predictive maintenance is crucial for the efficient operation of electrical motors, ensuring timely maintenance and preventing unexpected failures. This study proposes a Smart Maintenance System (SMAT) specifically designed for electrical motors, aimed at optimizing maintenance activities through predictive techniques. By utilizing mechanical vibration, electrical current, and motor body temperature sensors, the system monitors motor conditions in industrial applications such as pumps, generators, and other critical motor-driven systems to reduce unexpected downtime, lower maintenance costs, and extend motor lifespan. The sensor data is transmitted using Raspberry Pi and the TCP/IP communication protocol, with the data stored chronologically on a programmable interface controller. MATLAB is employed for data preprocessing, modeling, and prediction to facilitate maintenance decisions. A comparison of the K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) algorithms reveals accuracies ranging from 92.5% to 95.8% in classifying normal and failure conditions. The future enhancement of the system will focus on real-time data collection and improved prediction of motor conditions
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