Availability‐guaranteeing maintenance of series machine tools

Abstract

Abstract Condition monitoring enables transparency, as for example irregularities are detected automatically and reported. A condition forecast, however, requires more. In contrast to AI black box methods, frequently used in this context, a combination of existing expert knowledge and classical statistics is used as a method for a reliable determination of the remaining component‐lifetime. This works, if meaningful historical data are available in a sufficient quantity and quality. And this in turn requires a corresponding number of machines that are as identical in construction as possible and which must also be subject to a defined test regime, temporally closely monitored outside the production process. However, the quantity can be significantly smaller than the number of cases required for a prescient analysis of the correlations between a condition parameter and the wear condition of a specific component. The main target audience of the strategy presented here is therefore in particular manufacturers of series machines who wish to offer maintenance packages with corresponding availability guarantees and on‐site support for maintenance personnel.

Keywords

• augmented reality
• condition monitoring
• condition‐based maintenance
• linked data
• machine tool
• predictive maintenance