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The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 1-2/2022

02.02.2022 | ORIGINAL ARTICLE

A novel method for machine tool structure condition monitoring based on knowledge graph

verfasst von: Chaochao Qiu, Bin Li, Hongqi Liu, Songping He, Caihua Hao

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 1-2/2022

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Abstract

During a long-term structural health monitoring (SHM), large-scale machine tool structural response observed from various sensors show obvious big data characteristics. However, serious “data island” issues existing in traditional SHM inevitably limit the efficiencies of sensor data analysis. Besides, most existing identification methods for these diverse and complex signal data are manual methods, which are extremely time-consuming and inefficient. In this paper, a novel method based on knowledge graph (KG) was proposed to deal with the fine-grained domain knowledge modeling and multi-source sensor data integration problems in the field of machine tool SHM. With the help of KG-based querying and reasoning, it is more intelligent and convenient to retrieve and analyze sensor data. To verify the effectiveness of the proposed method, a long-term condition monitoring experiment was conducted on a CNC machine tool in an automobile factory. The dynamic properties of the machine tool structure were automatically identified based on KG, and a condition monitoring indicator based on the similarity of dynamic properties was applied to monitor the health condition of the machine tool. The final result showed the effectiveness of the KG for health monitoring of the machine tool structure.
Vorheriger Artikel Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method
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Metadaten
Titel
A novel method for machine tool structure condition monitoring based on knowledge graph
verfasst von
Chaochao Qiu
Bin Li
Hongqi Liu
Songping He
Caihua Hao
Publikationsdatum
02.02.2022
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 1-2/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-022-08757-5

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