Aiming at general abrupt faults of gas sensor

a method of gas sensor fault diagnosis is proposed based on principal component analysis（PCA） algorithm and artificial neural networks integrated via weight boosting（WB） algorithm.PCA method is used to exact fault feature

feature vectors are used as training samples

several ANN classifiers are trained in turn based on WB algorithm with these samples.Fault diagnosis result of sample under test is obtained by the artificial neural networks ensemble（ANNE） classifiers.Simulation results show that

identification correct rate of this method is more than 98.5 %

it can significantly improve fault diagnosis precision and generalization performance of gas sensors.