Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine
Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence reduces maintenance costs. The Motor Current Signature Analysis (MCSA) is considered as an effective fault detection method in any IM. However, a signal processing technique, which enhances the fault...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68841/1/Evaluation%20of%20Fourier%20and%20wavelet%20analysis%20for%20efficient%20recognition%20of%20broken%20rotor%20bar%20in%20squirrel-%20cage%20induction%20machine.pdf |
| _version_ | 1825933605836161024 |
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| author | Mehrjou, Mohammad Rezazadeh Mariun, Norman Marhaban, Mohammad Hamiruce Misron, Norhisam |
| author_facet | Mehrjou, Mohammad Rezazadeh Mariun, Norman Marhaban, Mohammad Hamiruce Misron, Norhisam |
| author_sort | Mehrjou, Mohammad Rezazadeh |
| collection | UPM |
| description | Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence reduces maintenance costs. The Motor Current Signature Analysis (MCSA) is considered as an effective fault detection method in any IM. However, a signal processing technique, which enhances the fault signature and suppress the dominant system dynamics and noise must be considered. Frequency analysis as well as time-frequency analysis is the most common signal processing methods. In this paper, the effectiveness of these two analysis methods were investigated for incipient broken rotor bar detection. Wavelet transform provides more accurate failure detection in different operational circumstances. However, there are different families in the wavelet analysis that affect the efficiency of encoding, denoising, compressing, decomposing and reconstructing the signal under observation. Accordingly, it is desirable to select the powerful wavelet family, which produces the best results for the signal being analyzed. This research also investigated the analysis of current signal using different families of wavelet for effective detection of broken rotor bars in IM. |
| first_indexed | 2024-03-06T09:59:55Z |
| format | Conference or Workshop Item |
| id | upm.eprints-68841 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:59:55Z |
| publishDate | 2010 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-688412019-06-10T03:48:57Z http://psasir.upm.edu.my/id/eprint/68841/ Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine Mehrjou, Mohammad Rezazadeh Mariun, Norman Marhaban, Mohammad Hamiruce Misron, Norhisam Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence reduces maintenance costs. The Motor Current Signature Analysis (MCSA) is considered as an effective fault detection method in any IM. However, a signal processing technique, which enhances the fault signature and suppress the dominant system dynamics and noise must be considered. Frequency analysis as well as time-frequency analysis is the most common signal processing methods. In this paper, the effectiveness of these two analysis methods were investigated for incipient broken rotor bar detection. Wavelet transform provides more accurate failure detection in different operational circumstances. However, there are different families in the wavelet analysis that affect the efficiency of encoding, denoising, compressing, decomposing and reconstructing the signal under observation. Accordingly, it is desirable to select the powerful wavelet family, which produces the best results for the signal being analyzed. This research also investigated the analysis of current signal using different families of wavelet for effective detection of broken rotor bars in IM. IEEE 2010 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68841/1/Evaluation%20of%20Fourier%20and%20wavelet%20analysis%20for%20efficient%20recognition%20of%20broken%20rotor%20bar%20in%20squirrel-%20cage%20induction%20machine.pdf Mehrjou, Mohammad Rezazadeh and Mariun, Norman and Marhaban, Mohammad Hamiruce and Misron, Norhisam (2010) Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine. In: 2010 IEEE International Conference on Power and Energy (PECon 2010), 29 Nov.-1 Dec. 2010, Kuala Lumpur, Malaysia. (pp. 740-743). 10.1109/PECON.2010.5697678 |
| spellingShingle | Mehrjou, Mohammad Rezazadeh Mariun, Norman Marhaban, Mohammad Hamiruce Misron, Norhisam Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title | Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title_full | Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title_fullStr | Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title_full_unstemmed | Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title_short | Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel- cage induction machine |
| title_sort | evaluation of fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel cage induction machine |
| url | http://psasir.upm.edu.my/id/eprint/68841/1/Evaluation%20of%20Fourier%20and%20wavelet%20analysis%20for%20efficient%20recognition%20of%20broken%20rotor%20bar%20in%20squirrel-%20cage%20induction%20machine.pdf |
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