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

Volume

40

Issue

2

fYear

1993

fDate

4/1/1993 12:00:00 AM

Firstpage

189

Lastpage

196

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;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.222640

Filename

222640