Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform （FFT） and continuous wavelet transfo...Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform （FFT） and continuous wavelet transform （CWT） of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.展开更多
An efficient tropical cyclone（TC） cloud image segmentation method is proposed by combining the curvelet transform,the cubic B-Spline curve,and the continuous wavelet transform.In order to enhance the global and loca...An efficient tropical cyclone（TC） cloud image segmentation method is proposed by combining the curvelet transform,the cubic B-Spline curve,and the continuous wavelet transform.In order to enhance the global and local contrast of the original TC cloud image,a second-generation discrete curvelet transform is implemented for the original TC cloud image.Based on our prior work,the low frequency components are enhanced by using an incomplete Beta transform and the genetic algorithm in the curvelet domain. Then the enhanced TC cloud image is used to segment the main body of the TC from the TC cloud image. First,pre-processing is implemented by B-Spline curves to the original TC cloud image to remove unrelated small cloud masses.A region of interest（ROI） which includes the main body of TC can thus be obtained. Second,the gray-level histogram of ROI is obtained.In order to reduce oscillations of the histogram,the gray-level histogram is smoothed by cubic B-Spline curves and the B-Spline histogram is obtained.The one dimensional continuous wavelet transform is employed for the curvature curve of the B-Spline histogram. A new segmentation cost criterion is given by combining threshold,error,and structure similarity.The optimally segmented image can be obtained by the criterion in the continuous wavelet domain.The optimally segmented image is post-processed to obtain the final segmented TC image.The experimental results show that the main body of TC can be effectively segmented from the complex background in the TC cloud image by the proposed algorithm.展开更多
文摘Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform （FFT） and continuous wavelet transform （CWT） of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.
基金Supported by the National Natural Science Foundation of China(40805048)Zhejiang Provincial Natural Science Foundation (Y506203)+2 种基金Shanghai Typhoon Institute/China Meteorological Administration(2008ST01)the State Key Laboratory of Severe Weather/Chinese Academy of Meteorological Sciences(2008LASW-B03)the Research Foundation of State Key Laboratory of Remote Sensing Science jointly sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University(2009KFJJ013)
文摘An efficient tropical cyclone（TC） cloud image segmentation method is proposed by combining the curvelet transform,the cubic B-Spline curve,and the continuous wavelet transform.In order to enhance the global and local contrast of the original TC cloud image,a second-generation discrete curvelet transform is implemented for the original TC cloud image.Based on our prior work,the low frequency components are enhanced by using an incomplete Beta transform and the genetic algorithm in the curvelet domain. Then the enhanced TC cloud image is used to segment the main body of the TC from the TC cloud image. First,pre-processing is implemented by B-Spline curves to the original TC cloud image to remove unrelated small cloud masses.A region of interest（ROI） which includes the main body of TC can thus be obtained. Second,the gray-level histogram of ROI is obtained.In order to reduce oscillations of the histogram,the gray-level histogram is smoothed by cubic B-Spline curves and the B-Spline histogram is obtained.The one dimensional continuous wavelet transform is employed for the curvature curve of the B-Spline histogram. A new segmentation cost criterion is given by combining threshold,error,and structure similarity.The optimally segmented image can be obtained by the criterion in the continuous wavelet domain.The optimally segmented image is post-processed to obtain the final segmented TC image.The experimental results show that the main body of TC can be effectively segmented from the complex background in the TC cloud image by the proposed algorithm.
