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排序方式: 共有164条查询结果,搜索用时 15 毫秒
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深水区的地震资料是采用长排列拖缆采集的,工区面积大,速度变化敏感,各向异性明显,传统人工速度分析技术不能满足工区的勘探开发需求,经研究,采用CGGVeritas的高密度双谱分析技术可以较好解决动校正问题,高效率高品质,提高了LSH深水地震资料的速度精度和密度,在全区得到推广应用,取得了很好的效果. 相似文献
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针对调制信号双谱(MSB)方法仅能处理平稳信号的不足,提出了一种基于加权平均集成经验模态分解(WAEEMD)和MSB的滚动轴承故障特征提取方法。首先,利用WAEEMD将滚动轴承的非平稳振动信号分解成一系列具有平稳特性的固有模态函数(IMF);然后,开发了一种基于Teager能量峭度(TEK)的加权平均方法以强调敏感IMF的重要性,并将加权后的IMF重构为WAEEMD滤波信号;最后,应用MSB分解WAEEMD滤波信号中的调制分量并提取故障特征频率。仿真和实验结果表明,相对于快速谱峭度(FK)和EEMD-MSB方法,WAEEMD-MSB方法能更准确地获取故障特征,从而验证了WAEEMD-MSB方法的有效性。 相似文献
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Bingnan PeiAuthor Vitae Zheng BaoAuthor VitaeMengdao XingAuthor Vitae 《Pattern recognition》2002,35(11):2643-2651
The paper starts by the definition of logarithm bispectrum and the presentation of its properties relative to target recognition. A relationship is established between the logarithm bispectrum feature of a target and that of its high resolution range profile. As a result, the criterion for target recognition, based on the relationship, is induced, and its scheme and pseudocode for implementation are given. Simulation with some acquired outfield data is given to show that the approach is effective and advantageous in both rapid recognition and easy computation. 相似文献
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The linearity of a time series is tested by use of the bispectrum. We define the time series to be linear if the best predictor is linear. The bispectrum is estimated by stretching the data and smoothing by the Subba Rao–Gabr optimal window. The null hypothesis tested here is that the best predictor is linear against the alternative that the best predictor is quadratic. It turns out that the test statistic is asymptotically χ2 -distributed under the hypothesis that the time series is linear. The results are demonstrated using simulated and real data. 相似文献
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利用信号围线积分双谱分形特征实现电台识别 总被引:1,自引:0,他引:1
信号的双谱能反映信号的细微特征,可用于电台识别中,但将它直接应用于电台识别需要计算复杂的匹配模板,增加分类器的复杂度,影响识别效率。针对此问题,提出了一种将信号围线积分双谱的分形特征作为电台特征参数的识别方法。首先由信号双谱估计值求出围线积分双谱,然后利用盒维数和信息维数定量描述围线积分双谱波形的复杂度,并将这两种分形维数作为特征向量,最后利用支持向量机(SVM)实现电台分类识别。对两部实际电台所发射的2FSK信号利用所提方法进行分析,结果表明在信噪比为7 dB及以上时,电台正确识别率能达到94.29%以上,验证了所提方法的可行性。 相似文献
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This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment. 相似文献
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Jean-Marc?Le?CaillecEmail author Rene?Garello 《Multidimensional Systems and Signal Processing》2005,16(1):49-84
In this paper, we derive the asymptotic bias and variance of conventional bispectrum estimates of 2-D signals. Two methods have been selected for the estimation: the first one – the indirect method – is the Fourier Transform of the weighted third order moment, while the second one – the direct method – is the expectation of the Fourier component product. Most of the developments are known for 1-D signals and the first contribution of this paper is the rigorous extension of the results to 2-D signals. The calculation of the bias of the direct method is a totally original contribution. Nevertheless, we did all calculations (bias and variance) for both method in order to be able to compare the results. The second contribution of this paper consists of the comparison of the theoretical bispectrum estimate bias and variance with the measured bias and variance for two 2-D signals. The first studied signal is the output of a non-minimal phase linear system driven by a non-symmetric noise. The second signal is the output of a non-linear system with Gaussian input data. In order to assess the results, we performed the comparison for both methods with different sets of parameters. We show that the maximum bias coefficient is the one of the 1-D case multiplied by the dimensionality of the signal for both methods. We also show that the estimate variance coefficient is the 1-D case coefficient with a power equal to the signal dimensionality.Received October 21, 2002; Revised December 2003; Accepted March 25, 2004; First Online Version published in December 2004 相似文献