多重信号分类(multiple signal classification,MUSIC)算法采用全向天线作为阵列阵元,为提升算法的抗干扰能力和定位精度,针对变电站局部放电检测采用定向天线阵列进行定位的具体应用,将天线方向图增益作为阵列流形系数,提出并推导了用...多重信号分类(multiple signal classification,MUSIC)算法采用全向天线作为阵列阵元,为提升算法的抗干扰能力和定位精度,针对变电站局部放电检测采用定向天线阵列进行定位的具体应用,将天线方向图增益作为阵列流形系数,提出并推导了用于定向天线阵列定位的MUSIC算法,在运用克拉美罗界和二阶统计信噪比估计理论分析算法定位误差基础上,通过搭建仿真模型进一步验证算法的性能。仿真结果表明,对于常规选定频带的局部放电信号,基于定向天线的MUSIC算法可在天线方向图增益大于1的来波方向范围内提升定位精度,且定位精度与天线增益大小成正相关。采用所设计的方向图增益达6 dB的定向天线阵列,在信噪比为0 dB的条件下信源定位误差为0.806°,而经典MUSIC算法的定位误差达到17.403°。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
Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the mach...Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.展开更多
文摘多重信号分类(multiple signal classification,MUSIC)算法采用全向天线作为阵列阵元,为提升算法的抗干扰能力和定位精度,针对变电站局部放电检测采用定向天线阵列进行定位的具体应用,将天线方向图增益作为阵列流形系数,提出并推导了用于定向天线阵列定位的MUSIC算法,在运用克拉美罗界和二阶统计信噪比估计理论分析算法定位误差基础上,通过搭建仿真模型进一步验证算法的性能。仿真结果表明,对于常规选定频带的局部放电信号,基于定向天线的MUSIC算法可在天线方向图增益大于1的来波方向范围内提升定位精度,且定位精度与天线增益大小成正相关。采用所设计的方向图增益达6 dB的定向天线阵列,在信噪比为0 dB的条件下信源定位误差为0.806°,而经典MUSIC算法的定位误差达到17.403°。
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.
文摘Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.
