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

•Adaptive Weighting Multi-classifier Fusion Decision Algorithm is proposed.•Rail crack recognition is conducted with MFL signals based on proposed method.•Proposed method well fusions MFL signals from multi-directions and multi-channels.•Proposed method has good performances in case of fewer training samples.•Proposed method has good performances in case of fewer channels of MFL signals. 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.