Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detecti
Neural networks application for induction motor faults diagnosis
Mathematics and Computers in Simulation - Special issue: Modelling and simulation of electrical machines, converters and systems
Identification of non linear MISO process using RKHS and Volterra models
WSEAS TRANSACTIONS on SYSTEMS
Modelling of a SISO and MIMO non linear communication channel using two modelling techniques
WSEAS Transactions on Circuits and Systems
Supervised learning with kernel methods
WAMUS'10 Proceedings of the 10th WSEAS international conference on Wavelet analysis and multirate systems
A new approach for identification of MIMO non linear system with RKHS model
WSEAS Transactions on Information Science and Applications
Artificial Intelligence Review
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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.