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Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis
Abstract
In this paper, a novel index is introduced for static and dynamic eccentricity fault diagnosis in permanent magnet synchronous motors (PMSMs). The proposed index is a linear combination of the energy, shape factor, peak, head angle of the peak, area below the peak, gradient of the peak of the detail signals in wavelet decomposition, and coefficients of the autoregressive model, which are extracted from the stator current signature analysis. Principal component analysis is applied to the features as the linear transform for dimension reduction and elimination of linear dependence between the features. In order to demonstrate the capability of these indexes to estimate eccentricity type and degree, the fuzzy support vector machine is employed as a classifier. Classification of the results indicates that the nominated index can be utilized to detect eccentricity occurrence, recognize its type, and determine its degree precisely. Since extraction of efficient indexes closely depends on precise computation of necessary signals, the time-stepping finite element method is utilized to model the PMSM under eccentricity fault and calculate the stator currents as a proper signal for processing. Simulation results are verified by the experimental results.
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Baudart, F., Dehez, B., Matagne, E., Telteu-Nedelcu, D., Alexandre, P., & Labrique, F. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2011.2170393 · Cited by 98
Research works printed but not linked: 37
- 10.1016/j.patrec.2004.06.009 · DOI · Paper page
- nelles, 2001, Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models
- 10.1006/mssp.1998.0177 · DOI
- mallat, 1998, A Wavelet Tour of Signal Processing
- 10.1109/TIE.2003.819682 · DOI
- 10.1109/72.788646 · DOI · Paper page
- 10.1177/1475921707081110 · DOI
- duda, 2002, Pattern Classification
- 10.1162/153244303322753616 · DOI
- 10.1109/20.703862 · DOI
- 10.2528/PIERC10071001 · DOI
- 10.1109/28.968184 · DOI
- 10.1109/TMAG.2003.816743 · DOI
- 10.1109/TMAG.2005.848321 · DOI
- 10.1109/TIE.2009.2030211 · DOI · Paper page
- 10.1109/TIE.2009.2033824 · DOI
- 10.1109/TIE.2011.2160514 · DOI
- rezig, 2010, Theoretical and experimental investigation of the effect of eccentricity faults on noise generation in brushless dc permanent magnet motors, Int Rev Elect Eng (IREE), 5, 1512
- 10.1109/TIE.2010.2060463 · DOI · Paper page
- 10.1109/TIE.2009.2029577 · DOI · Paper page
- 10.1109/TPEL.2006.886620 · DOI · Paper page
- 10.1049/ip-epa:19960155 · DOI
- 10.1109/TIE.2008.2011580 · DOI
- 10.1109/TIE.2011.2159356 · DOI
- 10.1109/TIE.2008.2004662 · DOI
- 10.1109/20.951316 · DOI
- 10.1109/TIE.2008.2007529 · DOI
- 10.1109/TMAG.2009.2012812 · DOI
- 10.1109/TIE.2010.2089937 · DOI · Paper page
- hawang, 2004, Comparison of performances between IPM and SPM motors with rotor eccentricity, J Magn Magn Mater, 282, 360, 10.1016/j.jmmm.2004.04.084 · DOI
- 10.1109/TIE.2010.2062480 · DOI · Paper page
- 10.1109/60.736320 · DOI
- 10.1109/TMAG.2009.2016416 · DOI
- 2005, OPERA-2d User Guide
- 10.1109/TMAG.2008.2001534 · DOI
- heller, 1977, Harmonic Field Effects in Induction Machines
- casadei, 2009, Magnets faults characterization for permanent magnet synchronous motors, Proc SDEMPED, 1
Cited by
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges
Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M., & Kowalski, C. T. (2022) · IEEE Access · doi:10.1109/access.2022.3180153 · Cited by 328
Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
Chen, Y., Liang, S., Li, W., Liang, H., & Wang, C. (2019) · Applied Sciences · doi:10.3390/app9102116 · Cited by 194
Choi, S., Haque, M. S., Tarek, M. T. B., Mulpuri, V., Duan, Y., Das, S., Garg, V., Ionel, D. M., Masrur, M. A., Mirafzal, B., et al. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2018.2819627 · Cited by 192
Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
Cai, B., Hao, K., Wang, Z., Yang, C., Kong, X., Liu, Z., Ji, R., & Liu, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115000 · Cited by 136
Metadata
- Authors
- Ebrahimi, B. M., Javan Roshtkhari, M., Faiz, J., & Khatami, S. V.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2014-04
- Volume
- 61
- Issue
- 4
- Pages
- 2041-2052
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2005184932
- Cited by
- 271
- References
- 1
- FWCI
- 26.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ebrahimi, B. M., Javan Roshtkhari, M., Faiz, J., & Khatami, S. V. (2014). Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2013.2263777
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