Fault diagnosis of motor based on low cost edge computing technology

The application of Industrial Internet of Things are growing rapidly. Intelligent device diagnosis with edge computing technology has an important potential for the equipment safety in smart factory. In this paper, we develop a low cost edge computing based motor fault diagnosis system. The proposed system uses the motor stator current signal for fault diagnosis. The frequency based MSAF-RATIO30 algorithm and KNN classifier are utilized to evaluate the type of fault. The experiment scenario includes three conditions of motor with 15 kinds of operating speed. The result validates the accuracy of proposed system can reach to 97.1%.