Continuous modelling of machine tool failure durations for improved production scheduling

Abstract

Unforeseen machine tool failures due to technical issues can cause downtimes leading to delays during production. To reduce delays, rescheduling of the production is, in most cases, necessary. However, warranting such a change requires reliable knowledge about the duration of the failure. This article presents a method to provide this knowledge by estimating the duration of a machine tool failure based on previous failure durations. Using the cross-industry standard process for data mining (CRISP-DM) and statistical methods, the embedded model for failure classification and duration is continuously improved. The method is thoroughly tested using multiple distributions, parameters and a practical use case. The results show high potential for predicting the duration of machine tool failures, which consequently could lead to improved quality of rescheduling. © 2020, The Author(s).