A RealTime Fault Early Warning Method for a HighSpeed EMU Axle Box Bearing An axle box bearing is one of the most important components of highspeed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a realtime fault early warning of a highspeed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for highspeed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in highspeed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMUODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the highspeed EMU in service, namely a MLSTMiForest (multilayer long shortterm memoryxe2x80x93isolation forest). First, the timeseries data of the temperaturerelated variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on highspeed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a highspeed EMU axle box bearing.

DataDriven Bias Correction and Defect Diagnosis Model for InService Vehicle Acceleration Measurements Track quality instruments use lowcost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are the main data source for track irregularity evaluation. However, considerable measurement bias exists in the vertical and lateral vibration data obtained from such instruments. False positive track vibration defects detected by track quality instruments occur frequently. This results in considerable time and effort being expended needlessly because maintenance workers have to visit the railway track sites to check and review the track vibration defects. Therefore, we propose a model for datadriven bias correction and defect diagnosis for inservice vehicle acceleration measurements based on track degradation characteristics. Substantial amounts of historical track measurement data from different inspection methods were mined extensively to eliminate the false positive detection of track vibration defects and diagnose the causes of track vibration defects. Actual measurement data from the Lanxin Railway were used to validate our proposed model. The success rate achieved in identifying false positive track vibration defects was 84.1%, and that in track vibration defect diagnosis was 75.8%. These high success rates suggest that the proposed model can be of practical use in improving railway track maintenance management.