Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Study of Fault Diagnosis of Hydro-Generator Unit Via Ga Nonlinear Principal Component Analysis Neural Network and Bayesian Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Qiao-ling Ji ; College of Power and Mechanical engineering, Wuhan University, Wuhan 430072, China. E-MAIL: stasy00@126.com ; Wei-Min Qi ; Wei-You Cai ; Yuan-Chu Cheng

Based on the complicated relationships between the symptoms and the defects of hydro-generator units, An approach to diagnosing the faults in hydro-generator units via a neural networks combined with Genetic algorithm (GA) and nonlinear principal analysis neural network (NLPCA NN) is presented in this paper. At first, GA optimizes both the structure and the connection of the NLPCA NN. The so-called GA-NLPCANN is employed to extract main features from high dimension samples. And then the Bayesian neural network (BNN) is also added to test the final diagnosis performance. Finally, the proposed scheme is applied to diagnose the faults samples of hydro-generator unit and the simulation results have proved the effectiveness of this method.

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006