The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 32-159
Full Name: Vilas Ghate
Position: Senior Lecturer
Age: ON
Sex: Male
Address: Electrical Engg Dept. Govt. College of Engineering Kathora Naka Amravati 444603 (MS)
Country: INDIA
Tel: +91 09765657809
Tel prefix:
Fax:
E-mail address: vng786@rediffmail.com
Other E-mails: vilas_ghate@rediffmail.com
Title of the Paper: Induction Machine Fault Detection Using Support Vector Machine Based Classifier
Authors as they appear in the Paper: Vilas N Ghate , Dr.Sanjay V Dudul
Email addresses of all the authors: svdudul@rediffmail.com
Number of paper pages: 14
Abstract: Industrial motors are subject to various faults which, if unnoticed, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. Generalized Feed Forward (GFFDNN) and Support Vector Machine (SVM) NN models are designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.
Keywords: Induction motor, Fault detection, Neural Network, GFFDNN, SVM, PCA
EXTENSION of the file: .pdf
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress:
IP ADDRESS: 210.212.166.245
