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


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.


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.


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