Hybrid Conditional Kernel SVM for Wire Rope Defect Recognition
Author:
Affiliation:
1. College of Engineering, Huazhong Agricultural University, Wuhan, China
2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hubei Province
Fundamental Research Funds for the Central Universities
National Key Laboratory of Electromagnetic Energy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10492621/10378657.pdf?arnumber=10378657
Reference32 articles.
1. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes
2. A comprehensive survey on support vector machine classification: Applications, challenges and trends
3. A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection
4. Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model
5. Neural network with adaptive evolutionary learning and cascaded support vector machine for fault localization and diagnosis in power distribution system
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