Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method ...Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.展开更多
In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows...In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows model and tunnel model. The first four cumulants of these two exclusion models are computed in a single barrier structure, and their characteristics are obtained. A scheme with the help of the first three cumulants is devised to check a transport process to follow the counter-flows model, the tunnel model or neither of them. Time series generated by Monte Carlo techniques is adopted to validate the abstraction procedure, and the result is reasonable.展开更多
In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and dis...In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.展开更多
基金supported by National Hi-tech Research and Development Program of China (863 Program,Grant No.2008AA042408)
文摘Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60676053)Applied Material in Xi’an Innovation Funds,China (Grant No. XA-AM-200603)
文摘In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows model and tunnel model. The first four cumulants of these two exclusion models are computed in a single barrier structure, and their characteristics are obtained. A scheme with the help of the first three cumulants is devised to check a transport process to follow the counter-flows model, the tunnel model or neither of them. Time series generated by Monte Carlo techniques is adopted to validate the abstraction procedure, and the result is reasonable.
文摘In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
