Part of this answer references GPT, GPT_Pro for better problem solving
Matlab is a high-level programming language for science and engineering calculations that can help implement the above formula. Bearing fault feature recognition based on Worlet wavelet and scale space can be realized by Matlab. Firstly, the simulation signal is constructed according to the above formula. Firstly, 4 is assigned as 1, @ is the number of shocks, ω is the resonance frequency caused by the fault, taking the value of 3000Hz, ζ is the damping coefficient, taking the value of 1 000 Ns/m, and T is the interval of fault shocks, taking the value of 144 Hz. x(t) assignment unit step function. Then, set the sampling frequency to 10 000 Hz and the data length to 10 000 sampling points. Finally, Gaussian white noise is added to the simulation signal.
f=1000; %采样频率 N=10000; %数据长度 A=1; %冲击振幅 num=50; %冲击个数 w=3000; %共振频率 zeta=1000; %阻尼系数 T=144; %冲击间隔 t=(0:N-1)/f; %时间矢量 x=zeros(1,N); %初始化单位阶跃函数 for i=1:num x=x+A*stepfun(t-i*T,-2); %单位阶跃函数加入故障冲击 end x=x+randn(1,N); %加入高斯白噪声
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