通过预处理滤除工频周期分量，消除了小波变换在信号峰值处的模极大值，从而避免了对故障的误判。
As wavelet module maxima occur at peaks of signal, the pretreatment removes power frequency periodic components from electric signal and thus avoids misjudgments of fault.
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.
通过对信号与噪声奇异性的分析，得出信号与噪声的小波变换模极大值在各个尺度上的表现截然相反的结论。
The singularities of signals and noises are investigated. A conclusion is drawn that the wavelet modulus maxima of signals have contrary behaviors from that of noises.
以二次光滑小波作为小波函数，对水果红外图像进行二维二进小波变换，计算了小波变换结果的局部模极大值点，从而得到了水果红外图像的轮廓。
A quadratic lubricity wavelet as wavelet function was defined to do the two dimensional dyadic wavelet transform on fruit infrared image, and to get local modulus maxima from the results.
以二次光滑小波作为小波函数，对水果红外图像进行二维二进小波变换，计算了小波变换结果的局部模极大值点，从而得到了水果红外图像的轮廓。
A quadratic lubricity wavelet as wavelet function was defined to do the two dimensional dyadic wavelet transform on fruit infrared image, and to get local modulus maxima from the results.
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