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Open AccessArticle

Machine Tool Component Health Identification with Unsupervised Learning

1
Institute of Machine Tools and Manufacturing (IWF), ETH Zürich, CH-8092 Zurich, Switzerland
2
Agathon AG, CH-4512 Bellach, Switzerland
3
Inspire AG, ETH Zürich, CH-8005 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2020, 4(3), 86; https://doi.org/10.3390/jmmp4030086
Received: 31 July 2020 / Revised: 26 August 2020 / Accepted: 31 August 2020 / Published: 2 September 2020
(This article belongs to the Special Issue AI Applications in Smart and Advanced Manufacturing)
Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series. View Full-Text
Keywords: condition monitoring; machine learning; prognostics and health monitoring; unsupervised learning; machine tools; manufacturing condition monitoring; machine learning; prognostics and health monitoring; unsupervised learning; machine tools; manufacturing
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MDPI and ACS Style

Gittler, T.; Scholze, S.; Rupenyan, A.; Wegener, K. Machine Tool Component Health Identification with Unsupervised Learning. J. Manuf. Mater. Process. 2020, 4, 86.

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