Industrial Imbalanced Fault Diagnosis Method Based on Borderline SMOTE Integrated with NPE and CatBoost-Reference-Cited by-同舟云学术

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Industrial Imbalanced Fault Diagnosis Method Based on Borderline SMOTE Integrated with NPE and CatBoost

Published:2022-08-03 Issue: Volume: Page:
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Container-title:2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Zhu Qunxiong¹,Wang Xinwei¹,Zhang Ning¹,Xu Yuan¹,He Yanlin¹

Affiliation:

1. College of Information Science and Technology (CIST), Beijing University of Chemical Technology (BUCT),Beijing,China,100029

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9858328/9858343/09858431.pdf?arnumber=9858431

Reference14 articles.

1. A Novel Monitoring Strategy Combining the Advantages of NPE and GMM

2. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

3. Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

4. A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features

5. Supporting Telecommunication Alarm Management System With Trouble Ticket Prediction

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Tool State Recognition Based on POGNN-GRU under Unbalanced Data;Sensors;2024-08-22

2. Novel MSMOTE Combined with LSDA for Imbalanced Fault Diagnosis;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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