Fault classification by neural networks and fuzzy logic
- Department of Electrical Engineering, Auburn University, Alabama 36849-5201 (United States)
- Westinghouse Electric Corporation, 1310 Beulah Road, Pittsburgh, Pennsylvania 15235 (United States)
A neural fuzzy-based and a backpropagation neural network-based fault classifier for a three-phase motor will be described in this paper. In order to acquire knowledge, the neural fuzzy classifier incorporates a learning technique to automatically generate membership functions for fuzzy rules, and the backpropagation algorithm is used to train the neural network model. Therefore, in this paper, the preprocessing of signals, fuzzy and neural models, training methods, implementations for real-time response and testing results will be discussed in detail. Furthermore, the generalization capabilities of the neural fuzzy- and backpropagation-based classifiers for waveforms with varying magnitudes, frequencies, noises and positions of spikes and chops in a cycle of a sine wave will be investigated, and the computation requirements needed to achieve real-time response for both fuzzy and neural methods will be compared. {copyright} 1995 {ital American} {ital Institute} {ital of} {ital Physics}
- OSTI ID:
- 165141
- Journal Information:
- AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 325; ISSN 0094-243X; ISSN APCPCS
- Country of Publication:
- United States
- Language:
- English
Similar Records
Self-organizing neural network as a fuzzy classifier
Nonlinear wind prediction using a fuzzy modular temporal neural network