A Data-driven Diagnosis Method for Voltage Sensor Intermittent Faults of Traction Inverter System

被引：0
|
作者：
Xiong W. [1 ]
Gou B. [2 ]
Zhang K. [2 ]
Zuo Y. [2 ]
Ge X. [1 ]
机构：
[1] Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Sichuan Province, Chengdu
[2] School of Electrical Engineering, Southwest Jiaotong University, Sichuan Province, Chengdu
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2024年 / 44卷 / 11期
基金：
中国国家自然科学基金;
关键词：
data-driven; extreme learning machine; inverter; nonlinear autoregression; sensor intermittent faults;
D O I：
10.13334/j.0258-8013.pcsee.230743
中图分类号：
学科分类号：
摘要：
Due to the randomness and unpredictability of intermittent faults, traditional fault diagnosis methods are difficult to apply to the diagnosis of intermittent faults. In order to improve the reliability of the traction inverter system, this paper proposes an intelligent diagnosis method for the intermittent faults of the DC side voltage sensor. First, combining the nonlinear autoregressive dynamic network structure and the extreme learning machine to quickly mine the nonlinear mapping relationship between historical data and the stator currents of the asynchronous motor, the stator currents predictor of the traction motor is obtained. Then, a sliding time window is designed to construct the currents residuals, and the occurrence and disappearance time of intermittent faults are detected to obtain the evaluation index that characterizes the severity of intermittent faults. The proposed method is verified based on the rapid control prototype (RCP) experimental platform. The results show that the current predictor has great robustness to dynamic conditions such as sudden load changes and sudden speed changes. The proposed diagnosis method can complete the detection of the occurrence times and disappearance times of intermittent faults within 0.65 ms and 0.9 ms, respectively, and can accurately identify the early, middle, and late stages of intermittent faults of sensors, realizing the evaluation of the severity of intermittent faults. © 2024 Chinese Society for Electrical Engineering. All rights reserved.
引用
收藏
页码：4446 / 4458
页数：12
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