On-line system for fault detection in induction machines based on wavelet convolution

J. Cusidó; J. A. Rosero; M. Cusidó; A. Garcia; J. A. Ortega; L. Romeral; Q. Author

doi:10.1109/PESC.2007.4342112

On-line system for fault detection in induction machines based on wavelet convolution

J. Cusidó^*
, J. A. Rosero
, M. Cusidó
, A. Garcia
, J. A. Ortega
, L. Romeral
, Q. Author

^*Corresponding author for this work

Polytechnic University of Catalonia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.

Original language	English
Title of host publication	PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference
Pages	927-932
Number of pages	6
DOIs	https://doi.org/10.1109/PESC.2007.4342112
Publication status	Published - 2007
Externally published	Yes
Event	PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference - Orlando, FL, United States Duration: 17 Jun 2007 → 21 Jun 2007

Publication series

Name	PESC Record - IEEE Annual Power Electronics Specialists Conference
ISSN (Print)	0275-9306

Conference

Conference	PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference
Country/Territory	United States
City	Orlando, FL
Period	17/06/07 → 21/06/07

Access to Document

10.1109/PESC.2007.4342112

Cite this

Cusidó, J., Rosero, J. A., Cusidó, M., Garcia, A., Ortega, J. A., Romeral, L., & Author, Q. (2007). On-line system for fault detection in induction machines based on wavelet convolution. In PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference (pp. 927-932). Article 4342112 (PESC Record - IEEE Annual Power Electronics Specialists Conference). https://doi.org/10.1109/PESC.2007.4342112

@inproceedings{572206cd15184d2c8ff9d5486dd0fc8e,

title = "On-line system for fault detection in induction machines based on wavelet convolution",

abstract = "Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.",

author = "J. Cusid{\'o} and Rosero, \{J. A.\} and M. Cusid{\'o} and A. Garcia and Ortega, \{J. A.\} and L. Romeral and Q. Author",

year = "2007",

doi = "10.1109/PESC.2007.4342112",

language = "English",

isbn = "1424406552",

series = "PESC Record - IEEE Annual Power Electronics Specialists Conference",

pages = "927--932",

booktitle = "PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference",

note = "PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference ; Conference date: 17-06-2007 Through 21-06-2007",

}

Cusidó, J, Rosero, JA, Cusidó, M, Garcia, A, Ortega, JA, Romeral, L & Author, Q 2007, On-line system for fault detection in induction machines based on wavelet convolution. in PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference., 4342112, PESC Record - IEEE Annual Power Electronics Specialists Conference, pp. 927-932, PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference, Orlando, FL, United States, 17/06/07. https://doi.org/10.1109/PESC.2007.4342112

On-line system for fault detection in induction machines based on wavelet convolution. / Cusidó, J.; Rosero, J. A.; Cusidó, M. et al.
PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference. 2007. p. 927-932 4342112 (PESC Record - IEEE Annual Power Electronics Specialists Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On-line system for fault detection in induction machines based on wavelet convolution

AU - Cusidó, J.

AU - Rosero, J. A.

AU - Cusidó, M.

AU - Garcia, A.

AU - Ortega, J. A.

AU - Romeral, L.

AU - Author, Q.

PY - 2007

Y1 - 2007

N2 - Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.

AB - Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.

UR - https://www.scopus.com/pages/publications/48349112339

U2 - 10.1109/PESC.2007.4342112

DO - 10.1109/PESC.2007.4342112

M3 - Conference contribution

AN - SCOPUS:48349112339

SN - 1424406552

SN - 9781424406555

T3 - PESC Record - IEEE Annual Power Electronics Specialists Conference

SP - 927

EP - 932

BT - PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference

T2 - PESC 07 - IEEE 38th Annual Power Electronics Specialists Conference

Y2 - 17 June 2007 through 21 June 2007

ER -

On-line system for fault detection in induction machines based on wavelet convolution

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