Design considerations for a motor fault detection artificial neural network

Author

Chow, Mo-Yuen ; Sharpe, Robert N. ; Hung, James C.

Author_Institution

Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA

fYear

1992

fDate

9-13 Nov 1992

Firstpage

1455

Abstract

The authors discuss the design considerations for a motor fault detection artificial neural network in terms of determining the input/output training data, the size of the training data set, network accuracy, robustness, implementation feasibility, and the number of input and hidden nodes to be used. A fuzzy logic approach to automating the network configuration process while simultaneously considering the accuracy, training time, sensitivity, and the number of neurons used in the implementation is also presented. Successful results have been obtained using artificial neural networks for motor fault detection and fuzzy logic in the network configuration design. A feedforward neural network for performing fault detection in a split-phase squirrel-cage induction motor is used for illustration purposes

Keywords

automatic testing; design engineering; fault location; machine testing; neural nets; squirrel cage motors; accuracy; artificial neural network; automatic testing; design; fault location; feedforward; fuzzy logic; implementation; machine testing; motor fault detection; network configuration process; neurons; nodes; robustness; sensitivity; split-phase squirrel-cage induction motor; training data; Artificial neural networks; Electrical fault detection; Fault detection; Fuzzy logic; Guidelines; Induction motors; Insulation; Neural networks; Robustness; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control., Proceedings of the 1992 International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0582-5

Type

conf

DOI

10.1109/IECON.1992.254387

Filename

254387