Condition monitoring system for machine tool auxiliaries

Abstract

Failures on machine tools not only occur on main components, but also on auxiliaries like cooling units or oil mist separators, which causes productivity losses similar to failures on main machine components. Due to their separation from the machine’s control network, their health status is in most cases not monitored. In this study, a new approach for online condition monitoring of auxiliary units by the example of an oil mist separator connected to a 5-axis machine tool is presented. The data is analyzed via machine learning principles in order to deduct an adequate condition assessment, encompassing environmental influences.