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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 12

Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

PANIGRAHY, P. S. See more information about PANIGRAHY, P. S. on SCOPUS See more information about PANIGRAHY, P. S. on IEEExplore See more information about PANIGRAHY, P. S. on Web of Science, CHATTOPADHYAY, P. See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on Web of Science
 
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Download PDF pdficon (8,079 KB) | Citation | Downloads: 1,065 | Views: 3,851

Author keywords
discrete wavelet transforms, fault diagnosis, feature extraction, induction motors, machine learning

References keywords
induction(17), fault(13), diagnosis(9), motors(8), detection(8), motor(7), analysis(7), wavelet(6), vibration(5), mining(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 95 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01012
Web of Science Accession Number: 000426449500012
SCOPUS ID: 85043281619

Abstract
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Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.


References | Cited By  «-- Click to see who has cited this paper

[1] S. Nandi, H. A. Toliyat, X. Li, "Condition monitoring and fault diagnosis of electrical motors-a review," IEEE trans. energy convers, vol. 20, pp. 719-729, 2005.
[CrossRef] [Web of Science Times Cited 1543] [SCOPUS Times Cited 1939]


[2] P. Konar, P. Chattopadhyay, "Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)," Appl. Soft Computing, vol. 11, pp. 4203-4211, 2011.
[CrossRef] [Web of Science Times Cited 335] [SCOPUS Times Cited 428]


[3] S. Sridhar, K. Uma Rao, S. Jade, "Detection of broken rotor bar fault in induction motor at various load conditions using wavelet transforms," IEEE Int. Conf. Recent Developments in Control, Automation and Power Engineering, 2015, pp. 77–82.
[CrossRef] [SCOPUS Times Cited 17]


[4] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets," IEEE Trans. Power Electronics, vol. 29, pp. 936-945, 2014.
[CrossRef] [Web of Science Times Cited 171] [SCOPUS Times Cited 196]


[5] P. A. Delgado-Arredondo, A. Garcia-Perez, D. Morinigo-Sotelo, "Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient," Shock and Vibration, vol. 2015, pp. 14, 2015.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 34]


[6] E. Cabal-Yepez, M. Valtierra-Rodriguez, R. J. Romero-Troncoso, A. Garcia-Perez, R.A. Osornio-Rios, H. Miranda-Vidales, R. Alvarez-Salas, "FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors," Mech. Sys. and Sig. Process, vol. 30, pp. 123-130, 2012.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 48]


[7] P. Konar, J. Sil, P. Chattopadhyay, "Knowledge extraction using data mining for multi-class fault diagnosis of induction motor," Neurocomputing, vol. 166, pp. 14-25, 2015.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 26]


[8] P. Konar, P. Chattopadhyay, "Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform," Appl. Soft Computing, vol. 30, pp. 341-352, 2015.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 88]


[9] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Vibration analysis based interturn fault diagnosis in induction machines," IEEE Trans. Ind. Inf, vol. 10, pp. 340-350, 2014.
[CrossRef] [Web of Science Times Cited 123] [SCOPUS Times Cited 136]


[10] R. J. Romero-Troncoso, R. Saucedo-Gallaga, E. Cabal-Yepez, A. Garcia-Perez, R. H. Osornio-Rios, R. Alvarez-Salas, H. Miranda-Vidales, N. Huber, "FPGA-based online detection of multiple combined faults in induction motors through information entropy and fuzzy inference," IEEE Trans. Ind. Elect, vol. 58, pp. 5263-5270, 2011.
[CrossRef] [Web of Science Times Cited 108] [SCOPUS Times Cited 134]


[11] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized bayesian inference," IEEE trans. neural networks and learning sys, vol. 25, pp. 990-1001, 2014.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 38]


[12] J. Seshadrinath, B. Singh, B. K. Panigrahi, "Single-turn fault detection in induction machine using complex-wavelet-based method," IEEE Trans. Ind. Appl, vol. 48, pp. 1846-1854, 2012.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 34]


[13] H. Garcia-Perez, R. d. J. Romero-Troncoso, E. Cabal-Yepez , R. A. Osornio-Rios, "The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors IEEE Trans. Ind. Electronics, vol. 58, pp. 2002-2010, 2011.
[CrossRef] [Web of Science Times Cited 180] [SCOPUS Times Cited 201]


[14] H. Ordaz-Moreno, R. d. J. Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, A. Garcia-Perez, "Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation," IEEE Trans. Ind. Elec, vol. 55, pp. 2193-2202, 2008.
[CrossRef] [Web of Science Times Cited 176] [SCOPUS Times Cited 214]


[15] E. Cabal-Yepez, A. G. Garcia-Ramirez, R. J. Romero-Troncoso, A. Garcia-Perez, Roque A. Osornio-Rios, "Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT," IEEE Trans. Ind. Inf, vol. 9, pp. 760-771, Jan. 2013.
[CrossRef] [Web of Science Times Cited 126] [SCOPUS Times Cited 150]


[16] H. Garcia-Perez, R. J. Romero-Troncoso, E. Cabal-Yepez, R. A. Osornio-Rios, J. d. J. Rangel-Magdaleno, H. Miranda, "Startup current analysis of incipient broken rotor bar in induction motors using high-resolution spectral analysis," IEEE Symp. Diagnostics for Electrical Machines, pp. 657-663, 2011.
[CrossRef] [SCOPUS Times Cited 41]


[17] H. M. Knight, S. P. Bertani, "Mechanical fault detection in a medium-sized induction motor using stator current monitoring," IEEE Trans. Energy Conv, vol. 20, pp. 753-760, 2005.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 114]


[18] P. Zhang, Y. Du, T. G. Habetler, B. Lu, "A survey of condition monitoring and protection methods for medium-voltage induction motors," IEEE Trans. Ind. Appl, vol. 47, pp. 34-46, 2011.
[CrossRef] [Web of Science Times Cited 495] [SCOPUS Times Cited 595]


[19] P. S. Panigrahy, P. Konar, P. Chattopadhyay, "Application of data mining in fault diagnosis of induction motor," IEEE Int. Conf. Control, Measurement and Instrumentation, 2016, pp. 274-278.
[CrossRef] [SCOPUS Times Cited 8]


[20] P. Konar, P. S. Panigrahy, P. Chattopadhyay, "Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor," Int. Conf. Mining Intelligence and Knowledge Exploration Springer International Publishing, 2015, pp. 553–562.
[CrossRef] [SCOPUS Times Cited 7]


[21] M. Kantardzic, Data mining: concepts, models, methods, and algorithms. Second Ed., John Wiley & Sons, 2011.
[CrossRef] [SCOPUS Times Cited 737]


[22] H. Jurek, Y. Bi, S. Wu, C. D. Nugent, "Clustering-based ensembles as an alternative to stacking," IEEE Trans. Knowledge and Data Eng, vol. 26, pp. 2120-2137, 2014.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 22]


[23] J. Xia, L. Bombrun, T. Adali, Y. Berthoumieu, C. Germain, "Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy," IEEE Trans. Geoscience and Remote Sensing, vol. 54, pp. 4971-4982, 2016.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 74]


[24] S. Dzeroski, B. Zenko. Is combining classifiers with stacking better than selecting the best one?. Machine learning, pp. 255-273, 2004.
[CrossRef] [Web of Science Times Cited 511] [SCOPUS Times Cited 667]


[25] S. Arlot, A. Celisse. A survey of cross-validation procedures for model selection. Statistics surveys, pp. 40-79, 2010.
[CrossRef] [Web of Science Times Cited 2417] [SCOPUS Times Cited 2688]


[26] Z. Deng, F. L. Chung, S. Wang, "Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization," IEEE Trans. on Fuzzy Systems, vol. 18, pp. 726-744, 2010.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 62]


[27] A. Ambarwari, Y. Herdiyeni and T. Djatna, "Combination of Relief Feature Selection and Fuzzy K-Nearest Neighbor for Plant Species Identification," Int. Conf. on Advanced Computer Science and Information Systems, 2016, pp. 315–320.
[CrossRef] [SCOPUS Times Cited 8]




References Weight

Web of Science® Citations for all references: 6,642 TCR
SCOPUS® Citations for all references: 8,706 TCR

Web of Science® Average Citations per reference: 237 ACR
SCOPUS® Average Citations per reference: 311 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-23 05:25 in 183 seconds.




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