【信号增强】基于广义结构化收缩算法（GSSA）实现振动信号弱特征增强附matlab代码和复现论文

1 简介

Vibration signal analysis has become one of the important methods for machinery fault diagnosis. Extraction of weak fault features from vibration signals with heavy background noise remains a challenging problem. In this paper, we first introduce the idea of algorithm-aware sparsity-assisted methods for fault feature enhancement, which extends model-aware sparsity-assisted fault diagnosis and allows a more flexible and convenient algorithm design. In the framework of algorithm-aware methods, we define the generalized structured shrinkage operators and construct the generalized structured shrinkage algorithm (GSSA) to overcome the disadvantages of l1-norm regularization based fault feature enhancement methods. We then perform a series of simulation studies and two experimental cases to verify the effectiveness of the proposed method. Additionally, comparisons with model-aware methods, including basis pursuit denoising and windowed-group-lasso, and fast kurtogram further verify the advantages of GSSA for weak fault feature enhancement.

2 部分代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clcclear allclose alladdpath(genpath(fileparts(mfilename('fullpath'))));rng('default')rng(19)  ​%% Figure initializationglobal FontSize FontName;Tstring =  'Time (s)'; Fstring = 'Frequency (Hz)';Astring =  'Amp (m/s^2)';FontSize = 11;   FontName = 'Times New Roman';MarkerSize = 4;  LineWidth = 1;%%FlagFigureAutoSave = 1;currentFolder = pwd;%% SimulationFs = 20480;N = 4096;mode = 'outer';% [Sig_Impulse , t] = MakeSignalBearing( Fs, N, 'outer');Sig_Impulse = QuasiPeiodicImpulseResponse_AM(N, Fs);t = (0 : N-1) / Fs;Sig_Cos = 0.5 * cos(2*pi*160*t') + 0.3 * cos(2*pi*320*t');​Sigma = 0.6;Noise = Sigma * randn(N , 1);Sig_Combine = Sig_Cos + Sig_Impulse' + Noise;% Sig_Combine = Sig_Cos + Sig_Impulse + Noise;[ yf2, f2 ] = Dofft( Sig_Combine , Fs , 0);​K = 10 : 10 : 1000;for i = 1 : length(K)    %% Setting the parameters    i    Q = 2;    r = 5;    J =10;    now = ComputeNow(N,Q,r,J,'radix2');    AH = @(Sig) tqwt_radix2(Sig, Q, r, J);       A = @(w) itqwt_radix2(w, Q, r , N);    lam = 1.0 * now;    rho = 1;    %% method1 : Generalized Structured Shrinkage    K1 = K(i);     Method1.Name = 'WGL';    Method1.Initial_Size = 5;    Method1.SubName = 'MC';    Method1.gamma = 2;    Method1.window = 'gausswin';    z1 = IterGSS(Sig_Combine, A, AH, lam, rho, K1, Method1);​​    %% method2 : TQWT-L1 based    Method2.Name = 'L1';    K2 = K(i);       z2 = IterGSS(Sig_Combine, A, AH, lam, rho, K2, Method2);​    %% method3 : Neighbor thresholding    params.Q = 2;    params.r = 5;    params.J =10;    K3 = K(i);       z3 = TQWTDe( Sig_Combine, params , 'nc', K3);​    %% method4 : Structured Shrinkage    Method3.Name = 'WGL';    Method3.Initial_Size = 5;    Method3.SubName = 'L1';    Method3.window = 'gausswin';    K4 = K(i);       z4 = IterGSS(Sig_Combine, A, AH, lam, rho, K4, Method3);     z1 = real(A(z1));    z2 = real(A(z2));    % z3 = real(A(z3));    z4 = real(A(z4));    %% Calculate RMSE    GST_RMSE(i) = RMSE(z1, Sig_Impulse);    L1_RMSE(i) = RMSE(z2, Sig_Impulse);    NC_RMSE(i) = RMSE(z3', Sig_Impulse);    ST_RMSE(i) = RMSE(z4, Sig_Impulse);endsave('Harmonic_Inference_Best_K.mat', 'K', 'GST_RMSE', 'L1_RMSE', 'NC_RMSE', 'ST_RMSE')

3 仿真结果

4 参考文献

[1] Zhao Z ,  Wang S ,  Xu W , et al. Sparsity-Assisted Fault Feature Enhancement: Algorithm-Aware Versus Model-Aware[J]. IEEE Transactions on Instrumentation and Measurement, 2020, PP(99):1-1.​

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