Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

Abstract

Permanent magnet synchronous motors (PMSM) have become one of the most substantial components of modern industrial drives. These motors, like all the others, can unfortunately undergo various failures, causing production line downtime and resulting losses. Accordingly, it is necessary to develop fault diagnostic techniques which detect the damages at the earliest possible stage. This study presents a method of detecting incipient faults of the PMSM stator windings using direct signal analysis and a convolutional neural network (CNN). During the tests, the structures of CNN were optimised to constitute a balance between the high efficiency of fault detection and a small number of network parameters. The effectiveness of the CNNs with inputs constituted by different electrical signals measured in the drive system is compared. Three raw data signals are tested as CNN inputs, namely: stator phase currents, phase-to-phase voltages and axial flux, without data preprocessing. The article aims to show the possibility of detecting the incipient interturn short circuits in the PMSM stator winding based on the information obtained directly from the measured signals as well as to present the influence of the drive operating conditions and the type of measurement signals used on the structure and performance of the developed CNNs.