An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

Boqiang Xu, Lingling Sun, Lie Xu, Guoyi Xu

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Simulated Annealing Algorithm (SAA). The performance of ESPRIT is tested with simulated stator current signal of an induction motor with BRB. It shows that even with a short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3kW, 380V, 50Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.
Original languageEnglish
Article numberTEC-00510-2011
Pages (from-to)654-660
Number of pages7
JournalIEEE Transactions on Energy Conversion
Volume27
Issue number3
DOIs
Publication statusPublished - Sep 2012

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

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