论文演讲稿。doc（Thesis speech. Doc）.docVIP

论文演讲稿。doc（Thesis speech. Doc） The harmonic wavelet decomposition algorithm is realized by fast Fu Liye transform (FFT) and inverse transform (IFFT). The algorithm is fast and accurate, so it has good engineering application value. Definition and properties of harmonic wavelet and complex Morlet wavelet The harmonic wavelet is defined as follows: W (omega) = {exp (i4 t -exp successfully (i2) t I2 t successfully successfully}/) Wavelet is defined as harmonic, which is a complex wavelet, compactly supported in the frequency domain and has a completely box spectrum. In fact, the part and the imaginary part are shown in figures 2, 2 (a) and (b) as shown in figure 2: (a) real part (b) imaginary part Fig. 2.2 real part and imaginary part waveform of harmonic wavelet Complex wavelet transform and the basic principle of its construction: In this paper, the complex Morlet wavelet (Complex, Morlet) is used. It is defined as: W (x) = 1/ (pi*fb) 1/2*e2ipifexe-x2/fb In the formula, the parameter FB is the bandwidth parameter, and the Fe is the wavelet central frequency. In Fig. 2.6 (a) and Fig. 2.6 (b), the waveform of the real part and the imaginary part of the complex Morlet wavelet (defined here is bandwidth and central frequency = FB = 1.5, FC = 1): [procedure see Appendix 6] (a) the real part (b) of complex Morlet and the imaginary part of complex Morlet Fig. 2.6 real part and imaginary part of complex Morlet wavelet The examples show: for the same fault vibration signal, if the complex Morlet wavelet to analyze, in the setting of the center frequency and bandwidth of FC FB parameter (here is assumed to be the default choice parameter setting in MATLAB cmor1-0.1, cmor1-0.5, cmor1-1, cmor1-1.5), by contrast, when using cmor1-1.5 the settings, can accurately determine the location of fault. Noise analysis combined with harmonic wavelet and approximate entropy for graduation design (Thesis) of Shanghai University of Engineering Science One