小波变换与MUSIC技术在感应电机转子断条故障检测中的比较研究

S. Bensaoucha, S. Bessedik, A. Ameur, S. Moreau, Ali Teta
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引用次数: 1

摘要

在鼠笼式感应电机中,转子断条故障的诊断是最困难的。本文特别关注了离散小波变换(DWT)来检测brb。此外,还提出了多信号分类(MUSIC)等高分辨率技术来改进brb检测。因此,本文研究了DWT和MUSIC技术在三相SCIM中检测brb的有效性。在这两种技术中,定子电流信号都是在稳态状态下进行分析,从而提取出brb故障频率。使用1.1 kW的SCIM获得的实验结果证实了MUSIC技术与DWT相比在提取brb相关故障频率方面的高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Study for Broken Rotor Bars Fault Detection in Induction Machine using DWT and MUSIC techniques
In Squirrel Cage Induction Machines (SCIMs), the diagnosis of Broken Rotor Bars (BRBs) fault is the most difficult. Special attention has been given to the Discrete Wavelet Transform (DWT) to detect the BRBs. Also, high-resolution techniques such as MUltiple SIgnal Classification (MUSIC) have been proposed to improve the BRBs detection. Therefore, this paper examines the effectiveness of DWT and MUSIC techniques to detect the BRBs in a three-phase SCIM. In both techniques, the stator current signal is analyzed in steady-state to extract the fault frequencies of BRBs.Experimental results obtained using a SCIM of 1.1 kW confirm the high performance of the MUSIC technique to extract the related-faults frequencies of BRBs compared to the DWT.
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