Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults. (1st February 2022) - Rooker, T.; Stammers, J.; Worden, K.; Potts, G.; Kerrigan, K.; Dervilis, N.
Abstract: Error motion trajectory data are routinely collected on multi-axis machine tools to assess their operational state. There is a wealth of literature devoted to advances in modelling, identification and correction using such data, as well as the collection and processing of alternative data streams for the purpose of machine tool condition monitoring. Until recently, there has been minimal focus on combining these two related fields. This paper presents a general approach to identifying both kinematic and non-kinematic faults in error motion trajectory data, by framing the issue as a generic pattern recognition problem. Because of the typically-sparse nature of datasets in this domain – due to their infrequent, offline collection procedures – the foundation of the approach involves training on a purely simulated dataset, which defines the theoretical fault-states observable in the trajectories. Ensemble methods are investigated and shown to improve the generalisation ability when predicting on experimental data. Machine tools often have unique 'signatures' which can significantly-affect their error motion trajectories, which are largely repeatable, but specific to the individual machine. As such, experimentally-obtained data will not necessarily be easily defined in a theoretical simulation. A transfer learning approach is introduced to incorporate experimentally-obtained error motion trajectories into classifiers which were trained primarily on a simulation domain. The approach was shown to significantly improve experimental test set performance, whilst also maintaining all theoretical information learned in the initial, simulation-only training phase. The ultimate approach represents a viable and powerful automated classifier for error motion trajectory data, which can encode theoretical fault-states with efficacy whilst also remain adaptable to machine-specific signatures. Highlights: Probing procedures for checking machine tool rotary-axis condition are ubiquitous. Various fault states can be identified through manual, expert analysis of the data. A machine learning approach is proposed to identify kinematic/non-kinematic faults. The approach is developed on simulated data and applied to an experimental setting. Ensemble and transfer learning improves performance on the experimental setting.