Suppr超能文献

基于振动信号包络分析的中压断路器故障诊断

Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis.

作者信息

Wu Yongbin, Zhang Jianzhong, Yuan Zhengxi, Chen Hao

机构信息

School of Electrical Engineering, Southeast University, Nanjing 210096, China.

Nanjing Power Supply Branch Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China.

出版信息

Sensors (Basel). 2023 Oct 9;23(19):8331. doi: 10.3390/s23198331.

Abstract

In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors and . The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down.

摘要

在现代电力系统或新能源发电站中,中压断路器(MVCB)变得越来越关键,而在线监测技术可极大提高MVCB的运行可靠性。本研究的目的是提出一种基于振动信号包络分析的故障诊断方法,包括离线故障特征训练和在线故障诊断。在离线故障特征训练期间,从MVCB的历史运行数据中提取振动信号的包络,然后使用小波包 - 能量谱构建典型故障特征向量。在在线故障诊断过程中,基于实时振动信号提取的包络构建故障特征向量,并通过特征向量之间的距离评估MVCB的状态。所提出的方法仅需处理振动信号的包络,这显著降低了信号带宽,进而可降低处理硬件和软件的成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e025/10575161/e6baab4f0814/sensors-23-08331-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验