Hierarchical Knowledge Guided Fault Intensity Diagnosis of Complex Industrial Systems-Reference-Cited by-同舟云学术

Hierarchical Knowledge Guided Fault Intensity Diagnosis of Complex Industrial Systems

Published:2024-08-24 Issue: Volume:9 Page:5657-5668
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Sha Yu¹^ORCID,Gou Shuiping²^ORCID,Liu Bo²^ORCID,Faber Johannes³^ORCID,Liu Ningtao²^ORCID,Schramm Stefan³^ORCID,Stoecker Horst⁴^ORCID,Steckenreiter Thomas⁵^ORCID,Vnucec Domagoj⁶^ORCID,Wetzstein Nadine⁶^ORCID,Widl Andreas⁶^ORCID,Zhou Kai⁷^ORCID

Affiliation:

1. Xidian University, FIAS and XF-IJRC, Xian, Shaanxi, China

2. Xidian University, Xian, Shaanxi, China

3. FIAS, Frankfurt, Hessian, Germany

4. FIAS, Goethe Universität and GSI, Frankfurt, Hessian, Germany

5. SAMSON AG, Frankfurt, Hessian, Germany

6. SAMSON AG, Feankfurt, Hessian, Germany

7. CUHK-SZ and FIAS, Shenzhen, Guangdong, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671610

Reference44 articles.

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4. A self-attention based contrastive learning method for bearing fault diagnosis

5. Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).