Defect inspection in stator windings of induction motors based on convolutional neural networks

Electric motors are subjected to many different quality control tests during their manufacture. Some of these tests are typically performed by human operators. It is well known in the literature that these operators are not reliable for repetitive inspections due to factors such as subjectivity and fatigue. Vision systems come as an alternative to perform visual tests for quality control. The authors have already proposed a vision system based on edge-detection tools to identify defects in electric motors characterized by one or more coil segments of the winding that are not properly fastened to the other coils and are placed in the projection of the orifice where the rotor is inserted. In this paper, a comparison between an improved version of this first algorithm and a convolutional neural network is done. Data augmentation is used to enhance the image data set, improving the reliability of network training. This dataset was also extrapolated to emulate the results of a manufacturing line. For a test dataset, neural networks presented better results than the edge detection algorithm, but their performance was similar for extrapolated images. For large production volumes, it is recommended the use of neural networks with proper training, but for small datasets the edge detection algorithm with proper parametrization is still the best choice.