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基于低压直流断路器的机械故障机理分析与特征提取技术研究 被引量:1

Research on Mechanical Fault Mechanism Analysis and Feature Extraction Technology Based on Low Voltage DC Circuit Breaker
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摘要 直流断路器是轨道交通直流牵引供电系统的核心保护设备,是直流供电系统安全运行的保证,确保其稳定运行至关重要。文中针对直流牵引断路器机械结构特点,进行了断路器机械故障模拟实验,选取分合闸线圈电流及振动信号作为监测物理量,研究其机械故障机理与特征提取方法。研制信号采集硬件装置,搭建直流断路器机械故障模拟实验平台,模拟6种常见的机械故障。采集不同故障下的线圈电流及振动信号进行特征提取后进行故障机理分析,为低压直流断路器机械故障诊断提供理论支撑。 DC circuit breaker is the core protection equipment of DC traction power supply system of rail transit and the guarantee of safe operation of DC power supply system,it is therefore very important to ensure its stable operation.As for the mechanical structure characteristics of DC traction circuit breaker in this paper,the mechanical fault simu⁃lation experiment of circuit breaker is performed.The current and vibration signals of the opening and closing coils are selected as the monitoring physical quantities to study its mechanical fault mechanism and feature extraction method.The hardware device of signal acquisition is developed,and the experimental platform of mechanical fault simulation of DC circuit breaker is set up to simulate six kinds of common mechanical faults.The coil current and vi⁃bration signals under different faults are collected for feature extraction and fault mechanism analysis,which can pro⁃vide theoretical support for mechanical fault diagnosis of low⁃voltage DC circuit breaker.
作者 叶奕君 郭嘉俊 张子健 吕文杰 艾泽光 崔鹏 谭佳明 杨爱军 王小华 荣命哲 YE Yijun;GUO Jiajun;ZHANG Zijian;LYU Wenjie;AI Zeguang;CUI Peng;TAN Jiaming;YANG Aijun;WANG Xiaohua;RONG Mingzhe(State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an 710049,China;Huaneng Henan Zhongyuan Gas Power Co.,Ltd.,Henan Zhumadian 463000,China)
出处 《高压电器》 CAS CSCD 北大核心 2024年第2期26-37,共12页 High Voltage Apparatus
基金 国家自然科学基金(U2166214) 陕西省重点研发计划(2022GXLH⁃01⁃11) 陕西省自然科学基础研究计划(2023⁃JC⁃JQ⁃41) 中国博士后科学基金(2022M712510) 电工材料电气绝缘全国重点实验室(EIPE23111,EIPE23408,EIPE23314)资助项目。
关键词 直流断路器 机械故障 电流信号 振动信号 特征提取 DC circuit breaker mechanical failure current signal vibration signal feature extraction
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