Novel Imbalanced Fault Diagnosis Method based on CSMOTE integrated with LSDA and LightGBM for Industrial Process
Author:
Affiliation:
1. College of Information Science & Technology (CIST), Beijing University of Chemical Technology (BUCT),Beijing,China,100029
Funder
the National Natural Science Foundation of China (NNSF)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9803856/9803880/09803941.pdf?arnumber=9803941
Reference13 articles.
1. Kernel extreme learning machine based hierarchical machine learning for multi-type and concurrent fault diagnosis
2. Incipient Fault Diagnosis for High-Speed Train Traction Systems via Stacked Generalization
3. Fault Diagnosis of Water Quality Monitoring Devices Based on Multiclass Support Vector Machines and Rule-Based Decision Trees
4. Fault Diagnosis Using Improved Discrimination Locality Preserving Projections Integrated With Sparse Autoencoder
5. Online Probabilistic Estimation of Sensor Faulty Signal in Industrial Processes and Its Applications
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