| 1/2018 - 12 |
Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault DiagnosisPANIGRAHY, P. S.   , CHATTOPADHYAY, P. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (8,079 KB) | Citation | Downloads: 1,168 | Views: 4,195 |
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
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 1597] [SCOPUS Times Cited 2008] [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 350] [SCOPUS Times Cited 454] [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. 7782. [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 173] [SCOPUS Times Cited 198] [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 32] [SCOPUS Times Cited 37] [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 42] [SCOPUS Times Cited 49] [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 23] [SCOPUS Times Cited 27] [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 78] [SCOPUS Times Cited 95] [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 126] [SCOPUS Times Cited 139] [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 110] [SCOPUS Times Cited 135] [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 31] [SCOPUS Times Cited 39] [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 35] [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 186] [SCOPUS Times Cited 205] [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 127] [SCOPUS Times Cited 157] [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 42] [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 520] [SCOPUS Times Cited 629] [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. 553562. [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 762] [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, "SpectralSpatial 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 65] [SCOPUS Times Cited 75] [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 549] [SCOPUS Times Cited 703] [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 2559] [SCOPUS Times Cited 2835] [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. 315320. [CrossRef] [SCOPUS Times Cited 8] Web of Science® Citations for all references: 6,943 TCR SCOPUS® Citations for all references: 9,076 TCR Web of Science® Average Citations per reference: 248 ACR SCOPUS® Average Citations per reference: 324 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 2024-12-04 04:03 in 185 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
