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Monitoring data‐based aging analysis for the high speed train axle
Author(s) -
Xie Guo,
Ye Minying,
Hei Xinhong,
Qian Fucai,
Cao Yuan,
Cai Baigen
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22527
Subject(s) - axle , bogie , automotive engineering , engineering , noise (video) , computer science , structural engineering , artificial intelligence , image (mathematics)
As the key component of the locomotive bogie, the axles have an important influence on the railway safety. The axles are usually maintained or replaced entirely and regularly after a certain running mileage or running time. This means that some damaged axles may not be replaced in time, and some others may be replaced before their running life, which not only poses potential safety risks but also result in wastage of resources. To address this problem, we propose an aging analysis method for high‐speed train axles based on the variation rate of axle temperature, which can diagnose each individual axle. The main steps in this are as follows: (i) preprocessing the original data to correct the isolated zero points and complementing the missing values; (ii) eliminating the measurement error and noise for the automatic extraction of the beginning and end of every temperature rising stage; (iii) calculating the temperature rising rate and evaluating the health status (or the aging level) of axle based on the temperature rising rate. Finally, the proposed method is validated based on the data from an actual operating line of a high‐speed train. The results demonstrate the effectiveness and practicability of the proposed method. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.