Based on the characteristics of wavelet transform, the wavelet Singularity Detection is used to locate the feature point and anomaly extension of the Rayleigh wave dispersion curve.
基于小波变换原理，利用小波分析中的奇异性检测方法来对瑞利波频散曲线的特征点和波动区段定位。
Wavelet singularity detection index is constructed by displacement. But in actual measurement, displacement error is larger. So it is difficulty to promote the apply in the Practical application.
小波奇异性检测指标是通过位移构造的，在实际测量中，位移的误差较大，造成实际推广使用有一定的难度。
Having good time-frequency localization character, and correctly identifying singularity point in fault signal, Wavelet is the main analysis method in this paper.
小波变换具有良好的时频局部性，具有变焦距的特点，对故障信号中的奇异点能够准确识别，是论文中应用的主要分析方法。
Wavelet is the best base of functions, with point singularity, and it has wide application in image processing because of its time-frequency localization and multiscale features.
小波是表示具有点奇异性函数的最优基，它由于具有时-频局部化特点和多尺度特性，在图像处理领域得到了广泛应用。
Based on analyzing the physical essence and the principle of singularity detection, this paper discusses the effect of wavelet bases and white noise on signal singularity detection.
在分析小波变换物理本质和奇异性检测原理的基础上，研究了小波基和白噪声对信号奇异性检测性能的影响。
It is an attractive application field for wavelet transform because the signal is expressed by the comprehensive behavior of wavelet transform of singularity spot in multi-dimensional space.
由信号小波变换的奇异点在多尺度上的综合表现来表示信号是小波变换引人注意的另一应用领域。
Wavelet analysis has space localization property. It is very effective to analyze the signal singularity and singularity location and scale by wavelet analysis.
小波分析具有空间局部化性质，利用小波分析来分析信号的奇异性及奇异性的位置和奇异度的大小是比较有效的。
And through gathering actual gear wheel vibration signal from laboratory, utilizing wavelet transform to study singularity monitoring, has made the good results.
并且通过从实验台采集到的实际齿轮振动信号分析，利用小波变换对其进行奇异性监测研究，取得了良好的效果。
The thesis has discussed the calculating of the Lipschitz exponent, and analysised and compared the condition between wavelet bases and singularity detection of signal.
文中对奇异性指数的求解问题进行了较详细的讨论和研究，同时对不同小波基下的信号奇异性检测情况进行了分析和比较。
Based on the wavelet transform and the relation between signal singularity and wavelet transform modular maximum, a noise removal technique is discussed.
在论述了小波分析、信号奇异性与小波变换模极大值性质关系的基础上，本文给出了一种基于小波变换模极大值的信号去噪算法。
This paper describes the principle of singularity detection of signals with wavelet transform. A fast algorithm is developed for computing wavelet transform of signals.
本文介绍了小波变换用于信号突变的检测原理，给出了实现小波变换的快速算法。
The new algorithm mainly recurred to the ability of detecting singularity in signal by continuous wavelet transform and multi-resolution analysis of wavelet transform.
算法充分利用连续小波变换探测信号奇异性的能力和小波的多分辨率特性。
The paper discuss the singularity detection theory of wavelet transform, this theory is applied to analysis of fault characteristics of the transient traveling waves.
本文讨论了小波变换及小波变换的奇异性检测理论，并将其应用于暂态行波故障特征的分析。
The application of wavelet theory in signal processing in mechanical engineering field is also discussed and a practical technique for signal singularity detection is proposed.
研究小波分析在机械工程信号处理中的应用，提出通过二进小波变换检测信号奇异点的实用技术。
A wavelet domain singularity detection algorithm, based on the theory of pulse extraction, is applied in partial discharge (PD) pulse extraction utilizing the singularity of PD signal.
在提出脉冲提取思想的基础上，利用局部放电信号所固有的奇异性特征，将小波变换的奇异性检测原理应用于提取局部放电脉冲。
If the wavelet transform is directly implemented in pitch detection, comparing the glottal closure singularity of speech signal with image grey break, we will not obtain the anticipative result.
将声门闭合在语音信号中表现出相应的奇异性，与图像边缘的灰阶突变进行等价对比，直接将小波变换用于声门闭合奇异型的检测，并不会得到预期效果。
If the wavelet transform is directly implemented in pitch detection, comparing the glottal closure singularity of speech signal with image grey break, we will not obtain the anticipative result.
将声门闭合在语音信号中表现出相应的奇异性，与图像边缘的灰阶突变进行等价对比，直接将小波变换用于声门闭合奇异型的检测，并不会得到预期效果。
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