The features of LMS adaptive filter in rotor bar broken diagnosis of squirrel cage asynchronous machines

被引：0
|
作者：
Liu X.-Z. [1 ]
He S.-H. [1 ]
Gao L. [1 ]
Zhang H.-C. [2 ]
机构：
[1] School of Electrical Engineering, Xi'an Jiaotong University, Xi'an
[2] Nanjing ThinkBon Electric Co., Ltd., Nanjing
来源：
Liu, Xin-Zheng | 1600年 / Editorial Department of Electric Machines and Control卷 / 21期
关键词：
Adaptive filter; Least mean square; Rotor bar broken; Spectrum analysis; Squirrel cage asychronous machine;
D O I：
10.15938/j.emc.2017.05.001
中图分类号：
学科分类号：
摘要：
For the diagnosis of rotor broken bar fault in squirrel cage asynchronous machines,spectrum analysis of stator current is often used to extract the side frequency components that represent the typical fault characteristics.To remedy the drawbacks existed in doing FFT spectrum analysis for the curent directly,which could make the side frequency components be overcovered by main frequency signal,the Least Mean Square (LMS) error adaptive filter algorithm was used prior to FFT analysis to filter main frequency signal and highlight the side frequency components.The LMS adaptive filter algorithm and the parameters determination in rotor broken bar fault diagnosis were given,and the sample features and the effective sample period for side frequency component at different operation slips were analyzed in detaile.The results show that,with LMS filter algorithm,when the stator current was sampled at a fixed sample period chosen according to main frequency,the side frequency component is actually sampled at the different point of its different individual waveform,and a full waveform is comopsed accrodingly.The effective sample period for side frequency component varies with slip and is smaller than the fixed one by an order of magnitude at least. © 2017, Harbin University of Science and Technology Publication. All right reserved.
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页码：1 / 7
页数：6
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