  This paper addresses the ability to enable machines to automatically detect
failures on machine tool components as well as estimating the severity of the
failures, which is a critical step towards autonomous production machines.
Extracting information about the severity of failures has been a substantial
part of classical, as well as Machine Learning based machine vision systems.
Efforts have been undertaken to automatically predict the severity of failures
on machine tool components for predictive maintenance purposes. Though, most
approaches only partly cover a completely automatic system from detecting
failures to the prognosis of their future severity. To the best of the authors
knowledge, this is the first time a vision-based system for defect detection
and prognosis of failures on metallic surfaces in general and on Ball Screw
Drives in specific has been proposed. The authors show that they can do both,
detect and prognose the evolution of a failure on the surface of a Ball Screw
Drive.
