Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements

TL;DR

This paper introduces a novel vision-based system capable of automatically detecting failures and predicting their severity on machine tool surfaces, specifically on Ball Screw Drives, advancing autonomous maintenance.

Contribution

It presents the first fully automatic vision-based method for defect detection and failure prognosis on metallic surfaces, focusing on Ball Screw Drives.

Findings

Successful detection of surface failures.

Effective prognosis of failure evolution.

First automatic system for this application.

Abstract

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.