针对风电机组齿轮箱在时变工况下的振动信号具有非平稳特性,提出一种谱峭度和Vold-kalman阶比跟踪(Vold-Kalman Filter Based Order Tracking,VKF-OT)相结合的故障特征提取方法。以转频和啮合频率作为VKF-OT的提取频率,获得随转速变化...针对风电机组齿轮箱在时变工况下的振动信号具有非平稳特性,提出一种谱峭度和Vold-kalman阶比跟踪(Vold-Kalman Filter Based Order Tracking,VKF-OT)相结合的故障特征提取方法。以转频和啮合频率作为VKF-OT的提取频率,获得随转速变化的阶比信号,通过阶比信号复包络直接求两种频率分量的幅值、相位,经实验分析这种方法能保留齿轮箱的瞬变信息。而后计算两种频率分量的谱峭度,以最大谱峭度对应的频率带能量与原阶比信号总能量之比作为故障特征,最后采用高斯混合模型对风电机组齿轮箱在不同工况下的150组振动信号进行特征描述,运用最大贝叶斯分类器实现故障识别。故障识别率表明该方法可有效地识别任意时变工况下的齿轮早期局部微弱故障。展开更多
A depth-averaged quasi single-phase mixture model is proposed for debris flows over inclined bed slopes based on the shallow water hydrosediment-morphodynamic theory with multi grain sizes. The stresses due to fluctua...A depth-averaged quasi single-phase mixture model is proposed for debris flows over inclined bed slopes based on the shallow water hydrosediment-morphodynamic theory with multi grain sizes. The stresses due to fluctuations are incorporated based on analogy to turbulent flows, as estimated using the depth-averaged k-? turbulence model and a modification component. A fully conservative numerical algorithm, using wellbalanced slope limited centred scheme, is deployed to solve the governing equations. The present quasi single-phase model using four closure relationships for the bed shear stresses is evaluated against USGS experimental debris flow and compared with traditional quasi single-phase models and a recent physically enhanced two-phase model. It is found that the present quasi single-phase model performs much better than the traditional models, and is attractive in terms of computational cost while the two-phase model performs even better appreciably.展开更多
The particle filter(PF) is a flexible and powerful sequential Monte Carlo(SMC) technique capable of modeling nonlinear,non-Gaussian, and nonstationary dynamical systems. However,the generic PF suffers from particle de...The particle filter(PF) is a flexible and powerful sequential Monte Carlo(SMC) technique capable of modeling nonlinear,non-Gaussian, and nonstationary dynamical systems. However,the generic PF suffers from particle degeneracy and sample impoverishment, which greatly affects its performance for nonlinear,non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model(IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest observation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further improve the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.展开更多
A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can i...A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can improve the accuracy of coherent point drift(CPD) by using the information of image feature.The objective function of the proposed method is defined by both of geometric spatial information and image feature information,and the origin Gaussian mixture model in CPD is modified according to the image feature of points.FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments.The registration error can be reduced efficiently by FB-CPD.Moreover,the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image.Compared with the original CPD,the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.展开更多
文摘针对风电机组齿轮箱在时变工况下的振动信号具有非平稳特性,提出一种谱峭度和Vold-kalman阶比跟踪(Vold-Kalman Filter Based Order Tracking,VKF-OT)相结合的故障特征提取方法。以转频和啮合频率作为VKF-OT的提取频率,获得随转速变化的阶比信号,通过阶比信号复包络直接求两种频率分量的幅值、相位,经实验分析这种方法能保留齿轮箱的瞬变信息。而后计算两种频率分量的谱峭度,以最大谱峭度对应的频率带能量与原阶比信号总能量之比作为故障特征,最后采用高斯混合模型对风电机组齿轮箱在不同工况下的150组振动信号进行特征描述,运用最大贝叶斯分类器实现故障识别。故障识别率表明该方法可有效地识别任意时变工况下的齿轮早期局部微弱故障。
基金funded by Natural Science Foundation of China(Grants Nos.51279144 and 11432015)
文摘A depth-averaged quasi single-phase mixture model is proposed for debris flows over inclined bed slopes based on the shallow water hydrosediment-morphodynamic theory with multi grain sizes. The stresses due to fluctuations are incorporated based on analogy to turbulent flows, as estimated using the depth-averaged k-? turbulence model and a modification component. A fully conservative numerical algorithm, using wellbalanced slope limited centred scheme, is deployed to solve the governing equations. The present quasi single-phase model using four closure relationships for the bed shear stresses is evaluated against USGS experimental debris flow and compared with traditional quasi single-phase models and a recent physically enhanced two-phase model. It is found that the present quasi single-phase model performs much better than the traditional models, and is attractive in terms of computational cost while the two-phase model performs even better appreciably.
基金supported by the National Natural Science Foundation of China(6127327561402517+3 种基金61573375)the Open Research Fund of State Key Laboratory of Astronautic Dynamics(2012ADL-DW0202)the Natural Science Foundation of Shaanxi Province of China(2013JQ8035)the China Postdoctoral Science Foundation(2013M542331)
文摘The particle filter(PF) is a flexible and powerful sequential Monte Carlo(SMC) technique capable of modeling nonlinear,non-Gaussian, and nonstationary dynamical systems. However,the generic PF suffers from particle degeneracy and sample impoverishment, which greatly affects its performance for nonlinear,non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model(IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest observation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further improve the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.
基金National Basic Research Program of China(973 Program)grant number:2010CB732505+1 种基金National Natural Science Foundation of Chinagrant number:30900380
文摘A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences.In this paper,we introduce a novel point correspondence method(FB-CPD),which can improve the accuracy of coherent point drift(CPD) by using the information of image feature.The objective function of the proposed method is defined by both of geometric spatial information and image feature information,and the origin Gaussian mixture model in CPD is modified according to the image feature of points.FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments.The registration error can be reduced efficiently by FB-CPD.Moreover,the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image.Compared with the original CPD,the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.
