Research Article
Leakage and Discharge Fault Detection Technology of Subway Electromechanical Equipment Based on Big Data Analysis
@INPROCEEDINGS{10.1007/978-3-031-50571-3_18, author={Wuguang Wang and XingfeiMa Ma}, title={Leakage and Discharge Fault Detection Technology of Subway Electromechanical Equipment Based on Big Data Analysis}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2024}, month={2}, keywords={Big Data Analytics Mechanical And Electrical Equipment The Subway Leakage Discharge Fault Feature Extraction Fault Detection}, doi={10.1007/978-3-031-50571-3_18} }- Wuguang Wang
XingfeiMa Ma
Year: 2024
Leakage and Discharge Fault Detection Technology of Subway Electromechanical Equipment Based on Big Data Analysis
ICMTEL
Springer
DOI: 10.1007/978-3-031-50571-3_18
Abstract
Due to the defects of its own components and the influence of external factors, the metro electromechanical equipment is prone to leakage and discharge faults, threatening the operation safety of the metro. Therefore, the leakage and discharge fault detection technology of metro electromechanical equipment based on big data analysis is proposed. Select appropriate sensors to integrate the operation signals of electromechanical equipment, explore the causes and specific types of leakage and discharge faults of subway electromechanical equipment, and on this basis, apply big data analysis technology, use wavelet transform algorithm to extract the operation signal characteristics and leakage and discharge fault signal characteristics of subway electromechanical equipment, combine support vector machine (SVM) algorithm, select appropriate kernel function, and obtain the optimal classification hyperplane, So as to realize the accurate detection of leakage and discharge faults of electromechanical equipment. The experimental data shows that the maximum success rate of leakage and discharge fault detection using this technology is 95.10%, which fully proves that the leakage and discharge detection performance of this technology is better.
