研究了信号的奇异性检测问题。
本文的主要研究工作就是叠加速度分析和奇异性检测。
This thesis is about the research of stack velocity analysis and singularity detection.
提出了一种用于故障信号奇异性检测的小波基选择方法一按照小波规则性系数选择小波基。
A method of choosing a wavelet for the detection of singularities of a fault signal is presented. The method is to choose a wavelet by the regularities of the wavelet and the signal.
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 paper discuss the singularity detection theory of wavelet transform, this theory is applied to analysis of fault characteristics of the transient traveling waves.
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。
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.
测得的信号低电阻电缆故障的分析表明：这种方法可减少偏差的奇异性检测，提高了故障定位的精度。
Analysis of measured signal of low resistance cable fault shows that: the method reduces deviation of singularity detection and improves precision of fault location.
文章给出了基于小波分析的信号奇异性检测理论，通过对突变信号进行小波分析可以发现结构的损伤情况。
The signal singularity detection theory based on wavelets analysis is given. Through the study of those singularity, the damage situation of the structure may be discovered.
基于信号的奇异性检测理论，利用小波分解的高频系数是否具有模极大值作为区分稳态和非稳态现象的判据。
Based on the theory of signal strangeness detect, the maxima modulus in high frequency of wavelet resolving can be used as a criterion to classify steady and non-steady state phenomena.
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。
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.
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。
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.
从工程应用的角度出发，进行信号的分解与重构、信号的奇异性检测、数据的压缩与重构、信号的消噪等若干方面的研究。
From the Angle of engineering application, the paper studies signals division and reconstruction, signals strangeness detection, data condensation and reconstruction, and signals noise reduction.
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。
The thesis has discussed the calculating of the Lipschitz exponent, and analysised and compared the condition between wavelet bases and singularity detection of 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。
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.
根据斜拉桥结构在振动时受损部位的振动信号的奇异性，利用小波奇异性检测理论分析斜拉桥结构在受损时的振动信号，判断结构损伤情况。
Based by singularity of vibration signal of damage position on cable-stayed bridge as vibrating, we can analyze vibration signal when cable-stayed bridge is damaged to estimate structure damage.
讨论了在纸幅随机纹理背景下纸病的检测，提出利用纸病处的奇异性来区分其和背景纹理。
This paper describes the paper defects detection in stochastic textures. Paper defects can be distinguished from the background texture by singularity characterization.
通过加入必要的回路检测，避免了幅度补偿后矩阵奇异性的产生，仿真结果表明了改进算法的有效性。
Necessary cycle detection is added to avoid the singular matrix appearing after gross error compensation,. Simulation results verify the effectiveness of modified algorithm.
本文阐述了利用小波分析来检测信号的奇异特征的原理，并对桩基础检测信号的奇异性进行了提取。
This paper illustrates principle of the peculiarity of signal test based on wavelet analysis, and extracts the peculiarity of signal test in pile foundation.
为了准确客观地检测出爆裂噪声的存在，提出了基于噪声信号奇异性的光耦器件爆裂噪声检测方法。
In order to detect the presence of burst noise in optocoupler device accurately and objectively, a new method was put forward based on singularity of noise.
通过对信号奇异性的检测和对信号的消噪，提取出了被检测信号的模极大值，进而实现了行波信号的故障测距。
By detecting signal strangeness and reducing signal noise, the paper arrives at the maximum value of the detected signal, thus the fault location of the traveling wave signal is achieved.
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。
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.
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