Identification of Subway Track Irregularities Based on Detection Data of Portable Detector

At present, the detection of subway track irregularities is mainly carried out by track inspection vehicles and track inspection trolleys. Such detections are restricted by subway service time, so they can only be carried out once every few months. This study explored the possibility of using the vibration of the vehicle body collected by a novel portable detector to detect track geometry irregularities. It makes a particular contribution to the dynamic detection of track conditions and the reduction of maintenance costs. Based on the data collected by the portable detector, wavelet transform was used to analyze the vibration of the vehicle body. The results confirmed that this method was effective in enhancing the correlation between vibration accelerations and track irregularities. Second, a data set processed by wavelet transform was resampled by a hybrid sampling method which uses clustering methods and considers data imbalance within each category. In this way, the imbalance ratio of the data set was found to be reduced without changing the original data set structure. Finally, the random forest algorithm and the gradient boost decision tree algorithm were adopted for classifying track regularity and irregularity data. The results showed that both two algorithms, especially the random forest algorithm, performed well for the longitudinal level track irregularity and the alignment track irregularity.