A Hybrid Model for Fault Diagnosis of Complex Systems
Song Yanxue · Electronics Optics & Control · 2009
In order to improve the fault diagnosis efficient of complex systems,a hybrid diagnostic model is put forward based on Genetic Algorithm(GA) and Artificial Neural Network(ANN).The diagnostics model runs through the following two steps: 1) using NN to preprocess the fault diagnosis data;and 2) using GA to diagnose.The model combines Nested Neural Network(NNN) and GA together,where NNN is used as a pre-processor which can reduce the quantity of fault types to be explored by the GA.The model overcomes the shortcoming of GA which can not judge reliabilities and that of NN which is difficult in identifying the most possible fault spot.It improves the accuracy,reliability,and consistency of the diagnosis result,and the total running time is also reduced to some extent.