Open-circuit fault diagnosis in voltage source inverter for motor drive by using deep neural network

To increase the reliability of motor drive system, many fault diagnosis approaches have been reported with regard to three-phase Pulse Width Modulation Voltage Source Inverter (PWM-VSI). Based on feature engineering and deep neural network, this paper proposes a fault diagnosis approach for the VSI in three-phase Permanent-magnet Synchronous Motor (PMSM) drive. The three-phase current signals are used for the fault diagnosis of VSI. 10 typical signal features are extracted from the three-phase current signals and used as the input of deep neural network. To improve the diagnostic performance, the network structure of the presented deep neural network is designed like a pyramid. Experimental results show that the presented method can detect not only the single open-circuit faults but also the double open-circuit faults in power switches, with high diagnostic accuracy (more than 95%). Besides, comparison results show that the presented method has strong generalization performance. This paper provides theoretical guidance for the fault diagnosis of VSI in PMSM.