Cluster Boundary Tracking

Reference

Unsupervised detection of state changes during operation of machine elements
Jonas Hillenbrand, Jürgen Fleischer
doi:10.36897/jme/136311

Example: Ball Screw - Motor Current vs. Temperature Clustering

Citation

BibTex:
@Article{Hillenbrand2021, journal=”Journal of Machine Engineering”, issn=”1895-7595”, volume=”21”, number=”2”, year=”2021”, title=”Unsupervised Detection of State Changes During Operation of Machine Elements”, abstract=”Interpretation of sensor data from machine elements is challenging, if no prior knowledge of the system is available. Evaluation methods must adapt surrounding conditions and operation modes. As supervised learning models can be time-consuming to set up, unsupervised learning poses as alternative solution. This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time. The approach is used to identify changing machine element states such as operating conditions and undesired changes, like incipient damage or wear. We show that knowledge about the evolving clusters can be used to identify operation and failure events. The approach is validated for machine elements with slide and roll contacts, such as ball screws and bearings. The data used has been captured using vibration and acoustic emission sensors. The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.”, author=”Hillenbrand, Jonas and Fleischer, Jürgen”, pages=”35–46”, doi=”10.36897/jme/136311”, url=”https://doi.org/10.36897/jme/136311” }