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2024年6期
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Fast evaluation on the fatigue level of copper contact wire based on laser induced breakdown spectroscopy and supervised machine learning for high speed railway
Fast evaluation on the fatigue level of copper contact wire based on laser induced breakdown spectroscopy and supervised machine learning for high speed railway
摘要
High-strength copper contact wire is of great significance to the electrified railway power supply system,which constantly provides electric power to the trains during operation.However,contact wire is subject to pressure,vibration,and natural forces such as wind,rain,ice,etc.which inevitably result in mechanical fatigue over time.This mechanical fatigue can lead to a decrease in the mechanical strength of the contact wire,and ulti-mately lead to problems such as wire detachment,fracture,or breakage,posing a serious safety hazard to the electrified railway system.Herein,the authors propose a strategy using nanosecond pulsed laser induced breakdown spectroscopy(LIBS)combined with machine learning technique to realise a fast evaluation on the fatigue level of copper contact line.Three different fatigue levels of copper samples have been made related with the requirement of operational conditions,and a total of 898 LIBS spectra were collected.Twenty-four combinations of spectral pre-processing,feature extraction,and optimisa-tion algorithms were used to compare the recognition results with the accuracy,recall rate,and time cost taken into accounted.Results have shown that the standard normal variable transform-principal component analysis-genetic algorithm improve support vector machine(SNV-PCA-GASVM)model have presented a most satisfactory per-formance than the others.The cross-validation accuracy of the SNV-PCA-GASVM model was 92.97％ while the dimensionality of input variables was reduced by 99.62％.This work is useful for the safety operation of power supply system in high speed railway,and technique development concerning the fast evaluation on materials fatigue in other industrial fields.
Author：
Wenfu Wei
Langyu Xia
Zefeng Yang
Huan Zhang
Like Pan
Jian Wu
Guangning Wu
作者单位：
School of Electrical Engineering,Southwest Jiao tong University,Chengdu,China
China Academy of Railway Sciences Corporation Limited,Beijing,China
School of Electrical Engineering,Xi'an Jiaotong University,Xi'an,China
年，卷(期)：
2024, 9(6)
机标分类号：
O357.5
R338.1
U211.5
在线出版日期：
2025年6月25日
