Online Diagnosis of Induction Motors Using MCSA
Jee-Hoon Jung
Jong-Jae Lee
Bong-Hwan Kwon
Dept. of Electron. & Electr. Eng., Pohang Univ. of Sci. & Technol.;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec. 2006
Volume: 53,
Issue: 6
On page(s): 1842-1852
ISSN: 0278-0046
INSPEC Accession Number: 9201854
Digital Object Identifier: 10.1109/TIE.2006.885131
Current Version Published: 2006-11-30
Abstract
In this paper, an online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed. MCSA is a method for motor diagnosis with stator-current signals. The proposed system diagnoses induction motors having four types of faults such as breakage of rotor bars and end rings, short-circuit of stator windings, bearing cracks, and air-gap eccentricity. Although MCSA is one of the most powerful online methods for diagnosing motor faults, it has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced signal-and-data-processing algorithms are proposed. They are composed of an optimal-slip-estimation algorithm, a proper-sample-selection algorithm, and a frequency auto search algorithm for achieving MCSA efficiently. The proposed system is able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results obtained on 3.7-kW and 30-kW three-phase squirrel-cage induction motors and voltage-source inverters with a vector-control technique are discussed
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