Online Recognition Method of Steel Rail Profile Based on Random Forest Algorithm
With the rapid development of railway transportation systems,the tasks of automated detection of non-contact steel rail wear have increased dramatically.However,the rich and diverse rail profiles and various outliers on the track lead to frequent errors in online wear calculation,and the massive amount of detection data from operating trains increase requirement for real-time detection.This paper proposes an online rail profile recognition method based on Random Forest algorithm(RF).Classification model one utilizes Principal Component Analysis(PCA)to extract the global features of the rail profile,while classification model two uses a relative height binary tree to extract the local features of the rail profile.Subsequently,the RF algorithm is used to classify and recognize the transformed low-dimensional feature vectors.Compared with the Support Vector Machine(SVM)classification recognition algorithm,the results show that the proposed classification model one based on RF algorithm achieves an overall recognition accuracy of 98.7%for common and non-common rail profiles,with a single-frame recognition time of 8.57 ms.Classification model two achieves an overall recognition accuracy of 96.7%for fishplate,switch point rail,and other profiles,with a single-frame recognition time of 11.95 ms.The proposed recognition method meets the real-time online detection requirements of rail profiles for operating trains at 75 km/h and thus it has certain engineering application value.
万方数据
维普
