Rail crack recognition based on Adaptive Weighting Multi-classifier Fusion Decision

In order to make the full use of three-dimensional information of Magnetic Flux Leakage (MFL) signals, an Adaptive Weighting Multi-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.