Title :
On the application and design of artificial neural networks for motor fault detection. II
Author :
Chow, Mo-Yuen ; Sharpe, Robert N. ; Hung, James C.
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
fDate :
4/1/1993 12:00:00 AM
Abstract :
For part I see ibid., vol.40, no.2, p.181-8 (1993). Some neural network design considerations, such as network performance, network implementation, size of training data set, assignment of training parameter values, and stopping criteria, are discussed. A fuzzy logic approach to configuring the network structure is presented, to automate the network design. Successful results are obtained from using artificial neural networks (ANNs) on motor fault detection and fuzzy logic in the network configuration design. It is concluded that these emerging technologies are promising for future widespread industrial usage
Keywords :
electric motors; feedforward neural nets; learning (artificial intelligence); power engineering computing; artificial neural networks; fault location; fuzzy logic approach; motor fault detection; stopping criteria; training data set; training parameter values; Artificial neural networks; Fault detection; Guidelines; Industrial training; Neural networks; Parameter estimation; Process control; Signal design; System testing; Training data;
Journal_Title :
Industrial Electronics, IEEE Transactions on
