Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach
| 1 | Ledmased Laboratory, University of Laghouat, 03000, Algeria |
| 2 | (LACoSERE) University of Laghouat, 03000, Algeria |
| 3 | IREENA, Saint Nazaire, Polytech’Nantes, France |
CORRESPONDING AUTHOR
Submission date: 2020-06-12
Final revision date: 2021-01-07
Acceptance date: 2021-02-03
Online publication date: 2021-02-04
Publication date: 2021-02-04
Diagnostyka 2021;22(1):57–63
KEYWORDS
spectral analysispartial rupture rotor bar(Fault diagnosisleast square support vector machine (LS-SVM))
TOPICS
Diagnostic systemsDiagnostics of buildingsTechnical diagnosticsDiagnostics of electronic systemsSignal analysis
ABSTRACT
This work attempts to clarify the potentials of Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to partial rupture rotor bar of the cage induction motor. Afterward the LS-SVM approach is established as monitoring system to detect the degree of broken rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
REFERENCES (15)
1.
Singh GK, Al Kazzaz SAS. Induction machine drive condition monitoring and diagnostic research-a survey. Electric Power Systems Research. 2003; 64: 145-158. https://doi.org/10.1016/S0378-....
2.
Sin ML, Soong WL, Ertugrul N. Induction machine on-line condition monitoring and fault diagnosis a survey, AUPEC2003, Australasian Universities Power Engineering Conference, Christchurch, New Zealand. 2003:1-6.
3.
Bhowmik SP, Pradhan S, Prakash M. Fault diagnostic and monitoring methods Of induction motor: a review. International Journal of Applied Control, Electrical and Electronics Engineering. (IJACEEE). 2013; 1(1).
4.
Bonnet AH, Soukup GC. Cause and analysis of stator and rotor failures in three phase squirrel cage induction motors. IEEE Transactions on Industry Application. 1992; 28(4)::921-937.
5.
Shi ZJ, Li G. Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management. 2011;52(4):1990-1998.https://doi.org/10.1016/j.enco....
6.
Yan W, Qiu H, Xue Y. Gaussian process for long-term time-series forecasting. Proceedings of the International Joint Conference on Neural Networks (IJCNN '09). 2009: 3420–3427.
7.
Melin P, Soto J, Castillo O, Soria J. A new approach for time series prediction using ensembles of ANFIS models. Expert Systems with Applications. 2012; 39(3):3494–3506. https://doi.org/10.1016/j.eswa....
8.
Suykens J, Gestel JV, Brabanter JD, Moor BD. Vandewalle J. Least Square Support Vector Machines. World Scientific Publishers, Singapore, 2002.
9.
Christianini N, Taylor J. Support vector machine and other kernel learning methods. London: Cambridge University Press, 2003.
10.
Birame M, Taibi D, Bessedik SA, Benkhoris MF. Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor. Diagnostyka. 2020; 21(4):35–41. https://doi.org/10.29354/diag/....
11.
Bessedik SA, Hadi H. Prediction of flashover voltage of insulators using least squares support vector machine with particle swarm optimisation. Electric Power Systems Research. 2013;104:87-92. https://doi.org/10.1016/j.epsr....
12.
Mahdjoubi A, Zegnini B, Belkheiri M, Prediction of critical flashover voltage of polluted Insulators under sec and rain conditions using least squares support vector machines (LS-SVM). Diagnostyka, 2019;20(1):49–54 https://doi.org/10.29354/diag/....
13.
Bensaoucha S, Bessedik S A,Ameur A and Teta A, Induction motors broken rotor bars detection using RPVM and neural network. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering. 2019;38(2):595:615. https://doi.org/10.1108/COMPEL....
14.
Bensaoucha S, Ameur A, Bessedik S A and Teta A,. Comparative Investigation of Broken Bar Fault Detectability in Induction Motor Through FFT and MUSIC Techniques. International Conference on Communications and Electrical Engineering (ICCEE). IEEE. 2018: 1-5.
15.
Laribi Souad. Use of Neuro-fuzzy technique in diagnosis of rotor faults of cage induction motor. 5th International Conference on Electrical Engineering (ICEE-B). Boumerdes, Algeria, 2017.
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