Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning
In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm. The proposed method is developed using experimental stator current data in the lab for two...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9417173/ |
| _version_ | 1819092565148827648 |
|---|---|
| author | Shafi Md Kawsar Zaman Xiaodong Liang Weixing Li |
| author_facet | Shafi Md Kawsar Zaman Xiaodong Liang Weixing Li |
| author_sort | Shafi Md Kawsar Zaman |
| collection | DOAJ |
| description | In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm. The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors fed by a VFD, subjected to healthy and faulty cases under various operating frequencies and motor loadings. The features are extracted from stator current signals using WPD by evaluating energy eigenvalues and feature coefficients at decomposition levels. The proposed method is validated by comparing with other graph-based semi-supervised learning (GSSL) algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). To enable fault diagnosis for untested motor operating conditions, mathematical equations to calculate features for untested cases are developed through surface fitting using features extracted from tested cases. |
| first_indexed | 2024-12-21T22:57:38Z |
| format | Article |
| id | doaj.art-0eb44d22f0344b199365c6d6d5e4d103 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T22:57:38Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0eb44d22f0344b199365c6d6d5e4d1032022-12-21T18:47:24ZengIEEEIEEE Access2169-35362021-01-019654906550210.1109/ACCESS.2021.30761499417173Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut LearningShafi Md Kawsar Zaman0https://orcid.org/0000-0001-9415-2619Xiaodong Liang1https://orcid.org/0000-0002-8089-5419Weixing Li2https://orcid.org/0000-0002-9965-9504Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, CanadaSchool of Electrical Engineering, Dalian University of Technology, Dalian, ChinaIn this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm. The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors fed by a VFD, subjected to healthy and faulty cases under various operating frequencies and motor loadings. The features are extracted from stator current signals using WPD by evaluating energy eigenvalues and feature coefficients at decomposition levels. The proposed method is validated by comparing with other graph-based semi-supervised learning (GSSL) algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). To enable fault diagnosis for untested motor operating conditions, mathematical equations to calculate features for untested cases are developed through surface fitting using features extracted from tested cases.https://ieeexplore.ieee.org/document/9417173/Graph-based semi-supervised learninggreedy-gradient max-cutinduction motorsvariable frequency drivewavelet packet decomposition |
| spellingShingle | Shafi Md Kawsar Zaman Xiaodong Liang Weixing Li Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning IEEE Access Graph-based semi-supervised learning greedy-gradient max-cut induction motors variable frequency drive wavelet packet decomposition |
| title | Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning |
| title_full | Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning |
| title_fullStr | Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning |
| title_full_unstemmed | Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning |
| title_short | Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning |
| title_sort | fault diagnosis for variable frequency drive fed induction motors using wavelet packet decomposition and greedy gradient max cut learning |
| topic | Graph-based semi-supervised learning greedy-gradient max-cut induction motors variable frequency drive wavelet packet decomposition |
| url | https://ieeexplore.ieee.org/document/9417173/ |
| work_keys_str_mv | AT shafimdkawsarzaman faultdiagnosisforvariablefrequencydrivefedinductionmotorsusingwaveletpacketdecompositionandgreedygradientmaxcutlearning AT xiaodongliang faultdiagnosisforvariablefrequencydrivefedinductionmotorsusingwaveletpacketdecompositionandgreedygradientmaxcutlearning AT weixingli faultdiagnosisforvariablefrequencydrivefedinductionmotorsusingwaveletpacketdecompositionandgreedygradientmaxcutlearning |