共查询到15条相似文献，搜索用时 109 毫秒
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本文采用经验模式分解 (EMD)提取信号的内在模函数 (IMF) ,并利用希尔伯特变换对所得IMF进行包络分析 ,提取机械故障特征。与直接对原信号进行包络分析相比较 ,该方法提取的机械故障特征更明显。数值模拟和对故障轴承振动信号分析表明了该方法的有效性。 相似文献
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振动信号处理技术能够提取反映结构健康的特征信息,在导管架平台健康监测中发挥了重要作用。为了给导管架平台健康监测与振动特征提取的发展提供借鉴和指导,文中对导管架平台特征提取中常用的信号处理方法:傅里叶变换、小波变换和希尔伯特-黄(Hilbert-Huang)变换进行了综述,系统总结了它们的基本理论、适用范围、优缺点以及在导管架平台健康监测中的应用情况。文中发现傅里叶变换方法简单、适用性强,对于平稳信号有很好的处理效果;小波变换和希尔伯特-黄变换能很好地提取非平稳信号的时频特性,在导管架平台健康监测与特征提取中应用广泛。同时,随着人工智能算法的快速发展,未来的发展中,可以进行信号处理方法与人工智能算法的结合使用,以期获得更好的信号处理效果。 相似文献
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为从海量的海洋原始噪声数据中快速和准确地提取海洋工程噪声信号,完善大型海洋工程的海洋噪声评估方法,文章提出基于声暴露级向量的海洋工程噪声信号自动提取方法,并进行实验验证。研究结果表明:基于声暴露级向量的信号自动提取方法实现的核心是计算疑似信号与样本信号声暴露级向量(包括声暴露级和有效时间长度)的标准化欧氏距离,结合变步长截取分析、半波包络双阈值触发和中心位置触发等方法,提高信号提取的效率和准确率;与传统的人工提取方法相比,该自动提取方法具有稳健、准确和高效的性能,可替代人工提取方法并广泛应用。 相似文献
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希尔伯特-黄变换(HHT)是近年来发展起来的一种新的时间序列信号分析方法.该文在对HHT深入研究与充分肯定的基础上,发展了信号的镜像闭合延拓和包络的极值延拓两种方法.通过几个典型的例子检验了两种方法,并与Huang等(1998,1999)进行了比较,得到了令人满意的结果.镜像闭合延拓法根据信号端点的分布特性,把镜子放在具有对称性的极值位置,通过镜像法把镜内信号映射成一个周期性的信号,不存在端点,从根本上避免了经验模态分解和希尔伯特变换的端点问题.极值延拓法简单易行,具有与镜像闭合法相当的效果,在处理非对称波形信号时更显其优越性. 相似文献
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小波变换突破了传统Fourier变换等信号处理方法均限制,具有表征信号局部特征的能力,信号的局部奇异性包含了信号的许多重要信息,论文研究了信号的奇异性检测问题。给出小波变换和信号奇异性的关系,实现小波分析对信号各类奇异间断点的有效检测,最后进行实例分析,说明此理论与方法适用于对边缘信号与突变信号的处理和提取,为海底底质识别提供理论依据。 相似文献
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本文概述了应用子波变换对悬浮物测量中的声散射信号进行处理的方法，利用子波变换对信号进行多尺度分解，降低噪声影响，提高信噪比，并对声散射信号降低噪声影响和提高悬浮物测量精度的研究进行了探讨。 相似文献
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本文采用经验模式分解 (EMD)与小波变换相结合的方法分析非平稳机械故障信号的奇异性 ,进行机械故障诊断。与直接对原信号进行小波分析相比较 ,该方法提取的奇异性特征明显。数值模拟和对故障轴承的振动信号分析表明了该方法的有效性。 相似文献
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针对海洋磁力测量数据中存在的高斯噪声,采用基于交叉证认的小波阈值去噪方法进行噪声去除。首先对磁力数据进行小波分解,利用交叉证认方法自动识别小波分解的信号层与噪声层,然后对噪声层小波系数进行阈值处理,进一步提取噪声层中的有用信息,最后进行小波重构得到去噪信号。同时提出了一种改进阈值处理函数,能够改善传统软、硬阈值函数存在的缺陷,提高重构信号的精度。实验分析表明,当噪声水平小于1.5nT时,相比于传统软、硬阈值函数的小波阈值去噪方法,该方法可更好地去除海洋磁力数据中的高斯噪声,并可较好地保留数据中的有用细节信息,使数据质量得到提高。 相似文献
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Walter J. Senus 《Marine Geodesy》2013,36(3):263-288
The current accuracy of sea‐going and airborne gravity measurements is not bounded by the precision of the gravimeter but by the precision with which external parameters such as vehicle velocity (speed), azimuthal heading, and geographic position can be determined. Uncertainties in these parameters are summed up in the Eötvös correction in the reduction of the measured data. This work investigates the suitability of baseline navigation systems, in particular Loran‐C and Omega, to further reduce the uncertainty of the Eötvös correction. Emphasis is placed on the velocity measurement error. A new algorithm is developed which derives velocity based upon the change in hyperbolic (or circular) grid readings, as opposed to the standard change in geographic position technique. A comparative analysis shows the new algorithm to be as precise as the currently used conventional calculation. Further, this simplified technique is accompanied with a 20‐fold reduction in computational complexity. Application of the results presented in this paper to the Loran‐C and Omega systems shows a velocity determination capability of 0.1 knot over a six minute integration time, and 0.05 knot for a 15 minute integration time for Loran‐C. The minimum error attainable in Omega is 0.1 knot when determined over a 17 minute measurement time. Further precision can be gleaned by applying the calculated error as a correction to subsequent velocity calculations. 相似文献
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以双曲线定位原理实现了罗兰C／北斗组合导航，提出了HDOP的定义，以此分析了两系统组合前后的定位精度，并给出了定位精度的仿真。结果表明，组合后大大改善了罗兰C定位精度和定位可用范围，并弥补了北斗定位系统有源定位的不足。 相似文献
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Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea 总被引：1，自引：0，他引：1
In multi-resolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency contents. We remove the high-frequency content and reconstruct a new “de-noise” signal by using inverse wavelet transform. The calculation of tidal constituent phase-lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron-Network (ANN) model. The “de-noise” signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT), and harmonic analysis were used to analyze the characteristics of tidal data.Using only a very short-period data as a training data set in Artificial Neuron-Network Back-Propagate (ANN-BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1–5 years). The results also show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality. 相似文献
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Nsiri B. Chonavel T. Boucher J.-M. Nouze H. 《Oceanic Engineering, IEEE Journal of》2007,32(3):729-743
In seismic deconvolution, blind approaches must be considered in situations where reflectivity sequence, source wavelet signal, and noise power level are unknown. In the presence of long source wavelets, strong interference among the reflectors contributions makes the wavelet estimation and deconvolution more complicated. In this paper, we solve this problem in a two-step approach. First, we estimate a moving average (MA) truncated version of the wavelet by means of a stochastic expectation-maximization (SEM) algorithm. Then, we use Prony's method to improve the wavelet estimation accuracy by fitting an autoregressive moving average (ARMA) model with the initial truncated wavelet. Moreover, a solution to the wavelet initialization problem in the SEM algorithm is also proposed. Simulation and real-data experiment results show the significant improvement brought by this approach. 相似文献