Rooker, T., Stammers, J., Worden, K. et al. (3 more authors) (2022) Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults. Mechanical Systems and Signal Processing, 164. 108271. ISSN 0888-3270
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Multi-axis machining; Error motion trajectory/volumetric error; Machine tool condition monitoring; Ensemble learning; Transfer learning |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) The University of Sheffield > Advanced Manufacturing Institute (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Aug 2021 10:52 |
Last Modified: | 11 Aug 2021 10:52 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ymssp.2021.108271 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176944 |