A Study of Diesel Engine Bearing Failure Based on Wavelet and Bagging-PNN Networks
Aiming at the problems of slow speed and low accuracy of diagnostic model for diesel engine fault,a diesel engine bearing fault diagnosis method based on wavelet and Bagging-PNN network is proposed.First,the sampled fault data are analyzed in time and frequency domains,and the data are denoised by wavelet analysis;then,the Bagging algorithm is fused with Probabilistic Neural Network(PNN),and the data obtained by multiple PNN classifiers voting in the same way are used as the final classification results.The output of the denoised data is used to establish a diesel engine bearing fault classification model to improve the diagnostic ac-curacy;finally,the comparison experiments show that the recognition accuracy of the wavelet and Bagging-PNN based diesel engine bearing fault diagnosis method is significantly improved.
NETL
NSTL
万方数据
