Study of fault diagnosis method based on ensemble-multi-SVM classifiers
- Resource Type
- Conference
- Authors
- Lv, Feng; Li, Xiang; Sun, Hao; Du, Hailian; Rong, Wenjie
- Source
- Proceedings of the 33rd Chinese Control Conference Control Conference (CCC), 2014 33rd Chinese. :3272-3276 Jul, 2014
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Accuracy
Fault diagnosis
Kernel
Training
Equations
Logistics
Fault Diagnosis
Ensemble Learning
Support Vector Machines
Classification
- Language
- ISSN
- 1934-1768
In order to improve the system accuracy of fault diagnosis, this paper proposes the integrated fault diagnosis method based on multi-SVM classifiers. MultiBoost integrated learning method using the AdaBoost algorithm and Wagging algorithm composed of multiple integrated with a combination of base classifiers to improve the classification accuracy of the system. The simulation results show that the method used in network fault diagnosis system of classification module design, making fault diagnosis accuracy has been significantly improved.