Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis

Yasser Gritli, Sang Bin Lee, Fiorenzo Filippetti, Luca Zarri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Rotor fault diagnosis of induction machines is commonly realized through Motor Current Signature Analysis (MCSA), i.e., by classical spectrum analysis of the input currents. However, this technique may fail in the case of outer cage damage of double cage induction motors, due to the small current circulation in the rated operating conditions. The situation is more complicated under time-varying conditions, where the typical Rotor Fault Frequency Components (RFFCs), which appear in the phase current spectrum of faulty motors, are spread in a bandwidth proportional to the speed variation and are difficult to detect accurately. A new diagnosis method based on the combined use of Double-Frequency Sliding (DFS) and Discrete Wavelet Transform (DWT) is proposed here for the analysis of the stator phase current. A experimental study on a copper-made double cage separate end ring rotor shows the validity of the proposed approach, leading to an effective diagnosis procedure for fault detection in double cage motors under time-varying conditions.

Original languageEnglish
Title of host publication2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012
Pages1284-1290
Number of pages7
DOIs
Publication statusPublished - 2012 Dec 17
Event4th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2012 - Raleigh, NC, United States
Duration: 2012 Sep 152012 Sep 20

Other

Other4th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2012
CountryUnited States
CityRaleigh, NC
Period12/9/1512/9/20

Fingerprint

Squirrel cage motors
Wavelet analysis
Induction motors
Rotors
Discrete wavelet transforms
Fault detection
Spectrum analysis
Stators
Failure analysis
Copper
Bandwidth

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

Cite this

Gritli, Y., Lee, S. B., Filippetti, F., & Zarri, L. (2012). Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis. In 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012 (pp. 1284-1290). [6342668] https://doi.org/10.1109/ECCE.2012.6342668

Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis. / Gritli, Yasser; Lee, Sang Bin; Filippetti, Fiorenzo; Zarri, Luca.

2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012. 2012. p. 1284-1290 6342668.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gritli, Y, Lee, SB, Filippetti, F & Zarri, L 2012, Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis. in 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012., 6342668, pp. 1284-1290, 4th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2012, Raleigh, NC, United States, 12/9/15. https://doi.org/10.1109/ECCE.2012.6342668
Gritli Y, Lee SB, Filippetti F, Zarri L. Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis. In 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012. 2012. p. 1284-1290. 6342668 https://doi.org/10.1109/ECCE.2012.6342668
Gritli, Yasser ; Lee, Sang Bin ; Filippetti, Fiorenzo ; Zarri, Luca. / Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis. 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012. 2012. pp. 1284-1290
@inproceedings{e4c05b0b7fbd49ea8ef43dc548214900,
title = "Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis",
abstract = "Rotor fault diagnosis of induction machines is commonly realized through Motor Current Signature Analysis (MCSA), i.e., by classical spectrum analysis of the input currents. However, this technique may fail in the case of outer cage damage of double cage induction motors, due to the small current circulation in the rated operating conditions. The situation is more complicated under time-varying conditions, where the typical Rotor Fault Frequency Components (RFFCs), which appear in the phase current spectrum of faulty motors, are spread in a bandwidth proportional to the speed variation and are difficult to detect accurately. A new diagnosis method based on the combined use of Double-Frequency Sliding (DFS) and Discrete Wavelet Transform (DWT) is proposed here for the analysis of the stator phase current. A experimental study on a copper-made double cage separate end ring rotor shows the validity of the proposed approach, leading to an effective diagnosis procedure for fault detection in double cage motors under time-varying conditions.",
author = "Yasser Gritli and Lee, {Sang Bin} and Fiorenzo Filippetti and Luca Zarri",
year = "2012",
month = "12",
day = "17",
doi = "10.1109/ECCE.2012.6342668",
language = "English",
isbn = "9781467308014",
pages = "1284--1290",
booktitle = "2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012",

}

TY - GEN

T1 - Advanced diagnosis of outer cage damage in double squirrel cage induction motors under time-varying condition based on wavelet analysis

AU - Gritli, Yasser

AU - Lee, Sang Bin

AU - Filippetti, Fiorenzo

AU - Zarri, Luca

PY - 2012/12/17

Y1 - 2012/12/17

N2 - Rotor fault diagnosis of induction machines is commonly realized through Motor Current Signature Analysis (MCSA), i.e., by classical spectrum analysis of the input currents. However, this technique may fail in the case of outer cage damage of double cage induction motors, due to the small current circulation in the rated operating conditions. The situation is more complicated under time-varying conditions, where the typical Rotor Fault Frequency Components (RFFCs), which appear in the phase current spectrum of faulty motors, are spread in a bandwidth proportional to the speed variation and are difficult to detect accurately. A new diagnosis method based on the combined use of Double-Frequency Sliding (DFS) and Discrete Wavelet Transform (DWT) is proposed here for the analysis of the stator phase current. A experimental study on a copper-made double cage separate end ring rotor shows the validity of the proposed approach, leading to an effective diagnosis procedure for fault detection in double cage motors under time-varying conditions.

AB - Rotor fault diagnosis of induction machines is commonly realized through Motor Current Signature Analysis (MCSA), i.e., by classical spectrum analysis of the input currents. However, this technique may fail in the case of outer cage damage of double cage induction motors, due to the small current circulation in the rated operating conditions. The situation is more complicated under time-varying conditions, where the typical Rotor Fault Frequency Components (RFFCs), which appear in the phase current spectrum of faulty motors, are spread in a bandwidth proportional to the speed variation and are difficult to detect accurately. A new diagnosis method based on the combined use of Double-Frequency Sliding (DFS) and Discrete Wavelet Transform (DWT) is proposed here for the analysis of the stator phase current. A experimental study on a copper-made double cage separate end ring rotor shows the validity of the proposed approach, leading to an effective diagnosis procedure for fault detection in double cage motors under time-varying conditions.

UR - http://www.scopus.com/inward/record.url?scp=84870947730&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870947730&partnerID=8YFLogxK

U2 - 10.1109/ECCE.2012.6342668

DO - 10.1109/ECCE.2012.6342668

M3 - Conference contribution

SN - 9781467308014

SP - 1284

EP - 1290

BT - 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012

ER -