Soft Computing Approach to detect Fault in Induction Motor

Pratap Sekhar Puhan*, Sudarsan Behera**
*Professor, Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Telangana, India.
**Assistant Professor, Department of Computer Science Engineering, Sreenidhi Institute of Science and Technology, Telangana, India.
Periodicity:October - December'2018
DOI : https://doi.org/10.26634/jee.12.2.14845

Abstract

Soft Computing based intelligent system plays an important role in the process of diagnosis and detection of different types of faults in induction motor in the recent age. This paper presents two soft computing approaches to detect and diagnosis of bearing fault in induction motor. Radial basis function and feed forward back propagation technique in neural network controller with Park's Transformation is used in this work to increases the accuracy level. First the Neural networks structure is formed by taking the number of neurons and various functions involved such as Tan sigmoid, log sigmoid. Taking stator current three phase signals from the Machine fault simulator converts it to two phase Signal using PARK'S Transformation. Then the model is tested and trained. The results shows that both FFBPN and RBFNN gives better results when PARK'S Transformation Implemented, However if it is minutely checked radial basis function technique in neural network gives more encouraging results which be utilize in Induction motor for setting of on line condition monitoring effectively.

Keywords

Induction Motor, Bearing Fault, Park's Transform, Back Propagation, Radial Basis Function.

How to Cite this Article?

Puhan, P. S., and Behera, S. (2018). Soft Computing Approach to Detect the Fault in Induction Motor. i-manager’s Journal on Electrical Engineering, 12(2), 6-14. https://doi.org/10.26634/jee.12.2.14845

References

[1]. Alguindigue, I. E., Loskiewicz-Buczak, A., & Uhrig, R. E. (1993). Monitoring and diagnosis of rolling element bearings using Artificial Neural Networks. IEEE Transactions on Industrial Electronics, 40(2), 209-217.
[2]. Amir, P. F. (2008). An adaptive input-output feedback linearization controller for doubly-fed induction machine drives. Serbian Journal of Electrical Engineering, 5(1), 139-154.
[3]. Broomhead, D., & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Int. J. Complex System, 2(3), 321-355.
[4]. Chou, B. (1991). Using Neural Neural Networks to detect incipient faults in induction motors. Journal of Neural Network Computing, 12(3).
[5]. Filippetti, F., Franceschini, G., Tassoni, C., & Vas, P. (2000). Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Transactions on Industrial Electronics, 47(5), 994-1004.
[6]. Khan, M. R., & Khan, M. F. (2015). Effect of resonance on the performance of single phase two winding self excited induction generator. i-manager's Journal on Electrical Engineering, 9(2), 8-15.
[7]. Knight, A. M., & Bertani, S. P. (2005). Mechanical fault detection in a medium-sized induction motor using stator current monitoring. IEEE Transactions on Energy Conversion, 20(4), 753-760.
[8]. Li, B., Chow, M. Y., Tipsuwan, Y., & Hung, J. C. (2000). Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 47(5), 1060-1069.
[9]. Nandi, S., & Toliyat, H. A. (1999). Condition monitoring and diagnosis of electrical machine. In A Review IEEE Ind. Application Society Annual Meeting.
[10]. Patel, R. K., & Giri, B. R. T. C. (2015a). Application of DWT and PDD for bearing fault diagnosis using vibration signal. J. Elect. Eng.,15 (4), 139-144.
[11]. Patel, R. K., & Giri, V. K. (2015b). Fault classification of induction motor bearing using statistical features and Artificial Neural Network. Journal on Electrical Engineering, 9(2),41-48.
[12]. Puhan, P. S., & Behera, S. (2017, December). Application of soft computing methods to detect fault in AC motor. In Advances in Computing, Communication and Control (ICAC3), 2017 International Conference on (pp. 1-5). IEEE.
[13]. Schoen, R. R., Habetler, T. G., Kamran, F., & Bartfield, R. G. (1995). Motor bearing damage detection using stator current monitoring. IEEE Transactions on Industry Applications, 31(6), 1274-1279.
[14]. Tavner, P. J., Gaydon, B. G., & Ward, D. M. (1986, May). Monitoring generators and large motors. In IEE Proceedings B-Electric Power Applications, 133(3), 169- 180.
[15]. Wu, S., & Chow, T. W. (2004). Induction machine fault detection using SOM-based RBF neural networks. IEEE Transactions on Industrial Electronics, 51(1), 183-194.

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