Journal
MEASUREMENT
Volume 123, Issue -, Pages 102-114Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2018.03.059
Keywords
Rail crack recognition; Magnetic Flux Leakage (MFL); Support Vector Machine (SVM); Adaptive weighting; Multi-classifier Fusion Decision
Funding
- Non-Destructive Testing and Monitoring Technology for High-Speed Transport Facilities Key Laboratory of Ministry of Industry and Information Technology of the People's Republic of China
- National Natural Science Foundation of China [61527803]
- Foundation of Graduate Innovation Center in NUAA-Application of Deep Learning on the processing of High Resolution Range Profile and Synthetic Aperture Radar Image [kfjj20170313]
- Fundamental Research Funds for the Central Universities
- Sub-topics of Major Scientific Instruments and Equipment Development Special of Ministry of Science and Technology of the People's Republic of China [2016YFF0103702]
- Ministry of Science and Technology of the People's Republic of China [2016YFB1100205]
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In order to make the full use of three-dimensional information of Magnetic Flux Leakage (MFL) signals, an Adaptive Weighting Mull-classifier Fusion Decision Algorithm is adopted for rail crack recognition. Support Vector Machine (SVM) is used to classify MFL signals from single-channel and single-direction, and then adaptive weightings of different SVMs are assigned according to entropy calculated by posterior probabilities of different SVMs. Finally, weighted majority vote strategy is used to make a comprehensive decision by fusing classification results of different channels and different directions. Effectiveness of the proposed method is testified by experiments based on measured MFL signals.
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