Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the M...Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the MSVD was utilized to extract the gravity anomaly associated with the gold mineralization in Tongshi gold field in the southwest of Shandong province.The results showed that the Tongshi complex with negative circular gravity anomaly is an important ore-controlling factor.And the positive ring gravity anomaly distributed展开更多
针对轴承早期微弱故障特征信息易被噪声掩盖和现实中难以获得大量典型故障样本的实际情况,提出了基于多分辨奇异值分解(Multi-Resolution Singular Value Decomposition,MRSVD)和变量预测模型模式识别(Variable Predictive Model based ...针对轴承早期微弱故障特征信息易被噪声掩盖和现实中难以获得大量典型故障样本的实际情况,提出了基于多分辨奇异值分解(Multi-Resolution Singular Value Decomposition,MRSVD)和变量预测模型模式识别(Variable Predictive Model based Class Discriminate,VPMCD)的轴承故障智能诊断方法。利用MRSVD对轴承加速度振动信号进行多层分解,提取包含故障特征的细节信息,建立对数正态分布模型,凸显细节信息中的非高斯特性,计算对数均值和对数标准差构造特征向量,并采用VPMCD方法进行故障识别。将该方法应用于实际轴承外圈、内圈、滚动体局部微弱故障状态下的故障诊断,结果显示:故障识别精度达到98.75%,证明了该方法的可行性和有效性。展开更多
为了从复杂工况下获取滚动轴承故障信息,提出了一种基于广义形态滤波和多分辨奇异值分解(MultiResolution Singular Value Decomposition,MRSVD)相结合的方法。首先利用广义形态学滤波方法对振动信号进行降噪预处理;然后利用MRSVD对降...为了从复杂工况下获取滚动轴承故障信息,提出了一种基于广义形态滤波和多分辨奇异值分解(MultiResolution Singular Value Decomposition,MRSVD)相结合的方法。首先利用广义形态学滤波方法对振动信号进行降噪预处理;然后利用MRSVD对降噪后的振动信号进行分解;最后通过峭度准则选取故障特征最丰富的细节信号,并对其进行Hilbert包络谱分析。将提出的方法应用于滚动轴承的故障检测,实验结果表明该方法能清晰地提取故障特征信息。展开更多
针对配电网行波定位中受噪声干扰导致波头标定困难和传统定位方法不适用于多分支配网结构的问题,提出基于多分辨率奇异值分解-变分模态分解MRSVD-VMD(multi-resolution singular value decomposition-variational mode decomposition)...针对配电网行波定位中受噪声干扰导致波头标定困难和传统定位方法不适用于多分支配网结构的问题,提出基于多分辨率奇异值分解-变分模态分解MRSVD-VMD(multi-resolution singular value decomposition-variational mode decomposition)的自适应波头标定算法和不受行波波速影响的T域定位算法。利用MRSVD和VMD分解故障行波,根据峭度值和峭熵比筛选有效分量,然后通过对称差分能量算子SDEO(symmetrical differencing energy operator)实现波头标定;最后利用行波到达时间筛选故障T域,实现故障点的区段定位和精确测距。仿真结果表明,MRSVD-VMD行波波头标定方法在不同噪声下能有效标定波头,T域定位算法排除波速影响,能实现多分支配电网故障的精确定位。展开更多
Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of...Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.展开更多
文摘Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the MSVD was utilized to extract the gravity anomaly associated with the gold mineralization in Tongshi gold field in the southwest of Shandong province.The results showed that the Tongshi complex with negative circular gravity anomaly is an important ore-controlling factor.And the positive ring gravity anomaly distributed
文摘针对轴承早期微弱故障特征信息易被噪声掩盖和现实中难以获得大量典型故障样本的实际情况,提出了基于多分辨奇异值分解(Multi-Resolution Singular Value Decomposition,MRSVD)和变量预测模型模式识别(Variable Predictive Model based Class Discriminate,VPMCD)的轴承故障智能诊断方法。利用MRSVD对轴承加速度振动信号进行多层分解,提取包含故障特征的细节信息,建立对数正态分布模型,凸显细节信息中的非高斯特性,计算对数均值和对数标准差构造特征向量,并采用VPMCD方法进行故障识别。将该方法应用于实际轴承外圈、内圈、滚动体局部微弱故障状态下的故障诊断,结果显示:故障识别精度达到98.75%,证明了该方法的可行性和有效性。
文摘为了从复杂工况下获取滚动轴承故障信息,提出了一种基于广义形态滤波和多分辨奇异值分解(MultiResolution Singular Value Decomposition,MRSVD)相结合的方法。首先利用广义形态学滤波方法对振动信号进行降噪预处理;然后利用MRSVD对降噪后的振动信号进行分解;最后通过峭度准则选取故障特征最丰富的细节信号,并对其进行Hilbert包络谱分析。将提出的方法应用于滚动轴承的故障检测,实验结果表明该方法能清晰地提取故障特征信息。
文摘针对配电网行波定位中受噪声干扰导致波头标定困难和传统定位方法不适用于多分支配网结构的问题,提出基于多分辨率奇异值分解-变分模态分解MRSVD-VMD(multi-resolution singular value decomposition-variational mode decomposition)的自适应波头标定算法和不受行波波速影响的T域定位算法。利用MRSVD和VMD分解故障行波,根据峭度值和峭熵比筛选有效分量,然后通过对称差分能量算子SDEO(symmetrical differencing energy operator)实现波头标定;最后利用行波到达时间筛选故障T域,实现故障点的区段定位和精确测距。仿真结果表明,MRSVD-VMD行波波头标定方法在不同噪声下能有效标定波头,T域定位算法排除波速影响,能实现多分支配电网故障的精确定位。
基金funded by the National Key Research and Development Plan (No. 2017YFB0202905)China Petroleum Corporation Technology Management Department “Deep-ultra-deep weak signal enhancement technology based on seismic physical simulation experiments”(No. 2017-5307073-000008-01)。
文摘Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.
