  The lack of reliable large datasets is one of the biggest difficulties of
using modern machine learning methods in the field of failure detection in the
manufacturing industry. In order to develop the function of failure
classification for ball screw surface, sufficient image data of surface
failures is necessary. When training a neural network model based on a small
dataset, the trained model may lack the generalization ability and may perform
poorly in practice. The main goal of this paper is to generate synthetic images
based on the generative adversarial network (GAN) to enlarge the image dataset
of ball screw surface failures. Pitting failure and rust failure are two
possible failure types on ball screw surface chosen in this paper to represent
the surface failure classes. The quality and diversity of generated images are
evaluated afterwards using qualitative methods including expert observation,
t-SNE visualization and the quantitative method of FID score. To verify whether
the GAN based generated images can increase failure classification performance,
the real image dataset was augmented and replaced by GAN based generated images
to do the classification task. The authors successfully created GAN based
images of ball screw surface failures which showed positive effect on
classification test performance.
