Papers by Author: Hao Chen

Abstract: In order to diagnose bearing fault with less samples,combined improved EEMD with SVM in the bearing fault intelligent diagnosis under low dimensional small sample is researched in this paper.It is applied to the binary classification and identification in bearing normal and fault state.The results show that depend only on less sample data 5d feature vector classification after training, SVM using linear kernel function and polynomial kernel function classification accuracy is still up to 100. classification accuracy under the less sample data in less 5d characteristic vectors by RBF kernel function under Sigmoid kernel function is relatively low.Choose appropriate SVM kernel function completely can realize low dimensional small sample right binary classification.

Abstract: In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear kernel function and polynomial kernel function is a hundred percent.Bearing normal and ball fault,normal and inner circle fault,normal and outer ring fault is completely correct apart.And there are the classification errors based on RBF kernel function and Sigmoid kernel functions.

Abstract: Intelligent diagnosis is the development direction of mechahnical condition monitoring and fault diagnosis.Conbined improved EEMD with SVM in fault intelligent diagnosis is researched in this paper.To bearing normal and fault as an example,impove EEMD decomposed 9D normalized energy for characteristic vector applied to the binary classification and identification.Compared to the SVM classification accuracy using different kernel functions that is linear,polynomial,RBF and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear and polynomial kernel function is a hundred percent.Bearing normal and fault two kinds of state is completely correct apart. And the normal and fault state of the binary classification and identification using RBF and Sigmoid kernel function appeared wtong points.

Abstract: The trend of the measured signal can not only reflect the influence of the external environment, and also reflect the performance of the machine itself mutations. Therefore, removing and extracting tendency item is the necessary link in signal pretreatment.In order to eliminate endpoint effect and modal aliasing phenomenon in EMD and EEMD, based on EEMD,improved EEMD is put forward and the improved EEMD in the application of the signal trend analysis is researched in this paper.In the measured signals to join in a ramp signals,With the improved EEMD decomposition extracted residual items,and the residual items with the original slope signal similarity analysis,the similarity is 0.975.compared to EMD extracted residual items similarity 0.898, EEMD extracted residual items similarity 0.961,the improved EEMD extracted residual item can more accurately reflect the trend of signal.

Abstract: In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

Abstract: In order to solve the endpoint effect and modal aliasing phenomenon in EMD and EEMD,Improved EEMD is put forward, and the application in signal singularity detection is researched in this paper. The improved EEMD will signal drops down into a series of different IMF to highlight the different local characteristics of original data, and then calculate Hilbert marginal spectrum and time-frequency spectrum to determine the frequency of these mutations and mutations position. To compared with FT, STFT, WVD,WT, EMD and EEMD etc, No cross-terms and no false IMF components are produced in the Hilbert time-frequency spectrum of the improved EEMD. Different frequency components and frequency mutations position are clearly distinguished at the same time. The Hilbert time-frequency spectrum of the improved EEMD has more superior detection signal singularity ability.