A Class-Aware Supervised Contrastive Graph Network for CNC machining condition monitoring with class-imbalanced data
Abstract
Machining state monitoring plays a critical role in ensuring quality, productivity, and reliability in CNC manufacturing. However, real-world industrial scenarios are often characterized by severe class imbalance and limited annotated data, which significantly challenge the robustness and generalization of existing learning-based approaches. To address this issue, we propose a Class-Aware Supervised Contrastive Graph Network (CSCGN), a unified framework that rethinks imbalanced industrial signal learning from a representation-centric perspective. Instead of treating signal segments independently, the proposed approach explicitly models the intrinsic structural relationships of one-dimensional sensor signals through a dynamic graph mechanism, while incorporating class-aware contrastive learning to enhance discriminative representation under imbalance. By jointly integrating multi-view signal representations, physics-informed topological modeling, and imbalance-aware optimization, the proposed framework effectively captures both structural dependencies and minority-class characteristics in noisy industrial environments. Extensive experiments on representative CNC machining tasks, including chatter detection and tool wear monitoring, demonstrate that CSCGN consistently outperforms state-of-the-art methods across various imbalance settings. The results highlight its strong capability in minority-class recognition while maintaining overall robustness, indicating its potential for reliable deployment in real-world industrial systems.
Details
- Organisationseinheit(en)
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Institut für Risiko und Zuverlässigkeit
- Externe Organisation(en)
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Shanghai Jiaotong University
Hong Kong Polytechnic University
Xi'an Jiaotong University
University of Technology Sydney
The University of Liverpool
Tongji University
- Typ
- Artikel
- Journal
- Mechanical Systems and Signal Processing
- Band
- 256
- ISSN
- 0888-3270
- Publikationsdatum
- 15.07.2026
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Steuerungs- und Systemtechnik, Signalverarbeitung, Tief- und Ingenieurbau, Luft- und Raumfahrttechnik, Maschinenbau, Angewandte Informatik
- Elektronische Version(en)
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https://doi.org/10.1016/j.ymssp.2026.114479 (Zugang:
Geschlossen
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