A Motor Rotary Fault Diagnosis System Using Dynamic Structural Neural Network

This study proposed an intelligent rotary fault diagnosis systems for motors. A sensor less rotational speed detection method and a dynamic structural neural network (DSNN) were used. This method can be employed to detect the rotary frequencies of motors with varying speeds and can enhance the discrimination of motor faults. To conduct the experiments, this work used wireless sensor nodes to transmit vibration data, and employed MATLAB to write codes for functional modules, including signal processing, sensor less rotational speed estimation, and neural networks. Additionally, Visual Basic was used to create an integrated human-machine interface. The experimental results regarding test equipment faults indicated that the proposed method can effectively estimate rotational speeds and provide superior discrimination of motor faults.