Simulation research on fault diagnosis using AdaBoost algorithm
Rui Yang · Jisuanji gongcheng yu sheji · 2005
AdaBoost is one of the most efficient toots to improve the predictive accuracy of any given learning algorithm.A multi-class fault diagnosis method was developed,which used three-layer perceptions as weak classifiers and combined them to create an aggregate hypothesis with AdaBoost iteration.The ordinary AdaBoost algorithm was modified to overcome noise sensitivity through reducing the weights of misclassified samples.A simulation experiment forthe gaspath componentsof aturbojetengine was conducted to demonstrate the effect of the method.24 groups of testing data from the turbojet engine were correctly classified into 5 fault classes.The simulation results show that the properties of the final fault classifier are enhanced in both generalization and robustness to noise.