Prediction of rotor slot width in induction motor using Dyadic wavelet transform and softmax regression
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J. Anish Kumar
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M. Gowthambigai
Abstract
Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Cao, W. Bradley, KJ. Assessing the impacts of rewind and repeated rewinds on induction motors: is an opportunity for re-designing the machine being wasted. IEEE Trans Ind Appl 2006;42:958–64. https://doi.org/10.1109/TIA.2006.876069.Search in Google Scholar
2. Djurovic, S, Crabtree, CJ, Tavner, PJ, Smith, AC. Condition monitoring of wind turbine induction generators with rotor electrical asymmetry. IET Renew Power Gener 2012;6:207. https://doi.org/10.1049/iet-rpg.2011.0168.Search in Google Scholar
3. Salles, G, Filippetti, F, Tassoni, C, Crellet, G, Franceschini, G. Monitoring of induction motor load by neural network techniques. IEEE Trans Power Electron 2000;15:762–8. https://doi.org/10.1109/63.849047.Search in Google Scholar
4. Ebrahimi, BM, Javan Roshtkhari, M, Faiz, J, Khatami, SV. Advanced eccentricity fault recognition in permanent magnet synchronous motors using stator current signature analysis. IEEE Trans Ind Electron 2014;61:2041–52. https://doi.org/10.1109/TIE.2013.2263777.Search in Google Scholar
5. Thomson, WT, Fenger, M. Current signature analysis to detect induction motor faults. IEEE Ind Appl Mag 2001;7:26–34. https://doi.org/10.1109/2943.930988.Search in Google Scholar
6. Zhang, P, Du, Y, Habetler, TG, Lu, B. A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl 2011;47:34–46. https://doi.org/10.1109/TIA.2010.2090839.Search in Google Scholar
7. Xie, Y, Wang, Z, Shan, X, Li, Y. Investigation of rotor thermal stress in squirrel cage induction motor with broken bar faults. COMPEL 2016;35:1865–86. https://doi.org/10.1108/COMPEL-10-2015-0372.Search in Google Scholar
8. Lei, Y, Jia, F, Lin, J, Xing, S, Ding, SX. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 2016;63:3137–47. https://doi.org/10.1109/TIE.2016.2519325.Search in Google Scholar
9. Chen, P, Xie, Y, Hu, S. Electromagnetic performance and diagnosis of induction motors with stator interturn fault. IEEE Trans Ind Appl 2021;57:1354–64. https://doi.org/10.1109/TIA.2020.3043214.Search in Google Scholar
10. Song, L, Wang, H and Chen, P. Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery. IEEE Trans Instrum Meas 2018;67:1887–99. https://doi.org/10.1109/TIM.2018.2806984.Search in Google Scholar
11. Du, Z, Chen, X, Zhang, H, Miao, H, Guo, Y, Yang, B. Feature identification with compressive measurements for machine fault diagnosis. IEEE Trans Instrum Meas 2016;65:977–87. https://doi.org/10.1109/TIM.2016.2521223.Search in Google Scholar
12. Frosini, L, Harlişca, C, Szabó, L. Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Trans Ind Electron 2015;62:1846–54. https://doi.org/10.1109/TIE.2014.2361115.Search in Google Scholar
13. Nejjari, H, Benbouzid, MEH. Monitoring and diagnosis of induction motors electrical faults using a current Park’s vector pattern learning approach. IEEE Trans Ind Appl 2000;36:730–5. https://doi.org/10.1109/28.845047.Search in Google Scholar
14. Concari, C, Franceschini, G, Tassoni, C. Differential diagnosis based on multivariable monitoring to assess induction machine rotor conditions. IEEE Trans Ind Electron 2008;55:4156–66. https://doi.org/10.1109/TIE.2008.2003212.Search in Google Scholar
15. Bellini, A, Filippetti, F, Franceschini, G, Tassoni, C, Kliman, GB. Quantitative evaluation of induction motor broken bars by means of electrical signature analysis. IEEE Trans Ind Appl 2001;37:1248–55. https://doi.org/10.1109/28.952499.Search in Google Scholar
16. Zhang, X, Liang, Y, Zhou, J, Zang, Y. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 2015;69:164–79. https://doi.org/10.1016/j.measurement.2015.03.017.Search in Google Scholar
17. Zhou, Z, Wu, QMJ, Wan, S, Sun, W, Sun, X. Integrating SIFT and CNN feature matching for partial-duplicate image detection. IEEE Trans Emerg Top Comput Intell 2020;4:593–604. https://doi.org/10.1109/TETCI.2019.2909936.Search in Google Scholar
18. Li, W, Mechefske, CK. Induction motor fault detection using vibration and stator current methods. Insight – Non-Destructive Testing and Condition Monitoring 2004;46:473–8. https://doi.org/10.1784/insi.46.8.473.39379.Search in Google Scholar
19. Lowe, DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.10.1023/B:VISI.0000029664.99615.94Search in Google Scholar
20. Chow, TWS, Shi, H. Induction machine fault diagnostic analysis with wavelet technique. IEEE Trans Ind Electron 2004;51:558–65. https://doi.org/10.1109/TIE.2004.825325.Search in Google Scholar
21. Rajamany, G, Srinivasan, S, Rajamany, K, Natarajan, RK. Induction motor stator interturn short circuit fault detection in accordance with line current sequence components using artificial neural network. J Electr Comput Eng 2019:1–11. https://doi.org/10.1155/2019/4825787.Search in Google Scholar
22. Wang, Y, Zhu, Z-Q, Feng, J, Guo, S, Li, Y, Wang, Y. Rotor stress analysis of high-speed permanent magnet machines with segmented magnets retained by carbon-fibre sleeve. IEEE Trans Energy Convers 2021;36:971–83. https://doi.org/10.1109/TEC.2020.3022475.Search in Google Scholar
23. Chen, P, Xie, Y, Li, D. Thermal field and stress analysis of induction motor with stator inter-turn fault. Machines 2022;107:504. https://doi.org/10.3390/machines10070504.Search in Google Scholar
24. Xie, Y, Wang, Z, Shan, X, Li, Y. Investigation of rotor thermal stress in squirrel cage induction motor with broken bar faults. COMPEL 2016;35:1865–86. https://doi.org/10.1108/compel-10-2015-0372.Search in Google Scholar
25. Adouni, A, Marques Cardoso, AJ. Thermal analysis of low-power three-phase induction motors operating under voltage unbalance and inter-turn short circuit faults. Machines 2021;9:2. https://doi.org/10.3390/machines9010002.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Special Issue on Smart Energy Infrastructures for Smart Cities
- Clearing method of regional power spot market based on blockchain and distributed data security reading algorithm
- Investigation on the supporting role of intelligent power system based on low carbon and environmental protection
- Energy consumption calculation and energy-saving measures of substation based on Multi-objective artificial bee colony algorithm
- Evaluation on the method of restoring the complex communication environment in the field based on the complex low pressure platform simulation platform
- Investigation for size and location of electric vehicle charging station accompanying VRP index and commissioning cost
- Research Articles
- Modeling of bidirectional electric vehicle charger for grid ancillary services
- Parameter identification of electric power remote telemetering system based on real-time section data and error-preventing topology analysis
- Prediction of rotor slot width in induction motor using Dyadic wavelet transform and softmax regression
- A novel ultra-high step-up interleaved DC–DC converter based on the three-winding coupled inductor for distributed generation power system
- Estimating and minimizing the eddy current loss in a permanent magnetic fault current limiter
- Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform
Articles in the same Issue
- Frontmatter
- Special Issue on Smart Energy Infrastructures for Smart Cities
- Clearing method of regional power spot market based on blockchain and distributed data security reading algorithm
- Investigation on the supporting role of intelligent power system based on low carbon and environmental protection
- Energy consumption calculation and energy-saving measures of substation based on Multi-objective artificial bee colony algorithm
- Evaluation on the method of restoring the complex communication environment in the field based on the complex low pressure platform simulation platform
- Investigation for size and location of electric vehicle charging station accompanying VRP index and commissioning cost
- Research Articles
- Modeling of bidirectional electric vehicle charger for grid ancillary services
- Parameter identification of electric power remote telemetering system based on real-time section data and error-preventing topology analysis
- Prediction of rotor slot width in induction motor using Dyadic wavelet transform and softmax regression
- A novel ultra-high step-up interleaved DC–DC converter based on the three-winding coupled inductor for distributed generation power system
- Estimating and minimizing the eddy current loss in a permanent magnetic fault current limiter
- Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform
