Machinery Monitoring and Maintenance System Using Decision Tree Model for Fault Prediction

This paper presents a design and implementation of the machinery monitoring and maintenance system. The proposed system has two advanced features including supervision of machinery in real-time and potential fault prediction by using decision tree model. The monitoring system is designed using IoT technologies to collect machine data including power consumption and operation states. Moreover, the potential faults of monitored machines are predicted by the proposed algorithm using decision tree model. The whole system has been implemented and applied in a real factory to demonstrate its performance.