Sensorless fault diagnosis of induction motors

被引：55

作者：

Kim, K ^{[1
]}

Parlos, AG

Bharadwaj, RM

机构：

[1] Honeywell Labs, Minneapolis, MN 55418 USA

[2] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USA

[3] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA

[4] Gen Elect Global Res Ctr, Niskayuna, NY 12309 USA

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2003年 / 50卷 / 05期

基金：

美国国家科学基金会;

关键词：

fault diagnosis; induction motors; neural networks (NNs); signal processing;

D O I：

10.1109/TIE.2003.817693

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance, and improved operational efficiency of induction motors: In this paper, a speed-sensorless fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks and multiresolution or Fourier-based signal processing for transient or quasi-steady-state' operation, respectively. In addition to nameplate information required for the initial system. setup, the proposed fault diagnosis system uses only motor terminal voltages and currents. The effectiveness of the proposed diagnosis system in detecting the most widely encountered motor electrical and mechanical faults is demonstrated through extensive staged faults. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2-, 373-, and 597-kW induction motors.

引用

页码：1038 / 1051

页数：14

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