数据不理想条件下的轴承故障诊断方法研究

function allmode=eemd(Y,Nstd,NE) % This is an EMD/EEMD program % % INPUT: % Y: Inputted data;1-d data only % Nstd: ratio of the standard deviation of the added noise and that of % Y; Nstd = (0.1 ~ 0.4)*std(Y). % NE: Ensemble number for the EEMD, NE = 10-50. % OUTPUT: % A matrix of N*(m+1) matrix, where N is the length of the input % data Y, and m=fix(log2(N))-1. Column 1 is the original data, columns 2, 3, ... % m are the IMFs from high to low frequency, and comlumn (m+1) is the % residual (over all trend). % % NOTE: % It should be noted that when Nstd is set to zero and NE is set to 1, the % program degenerates to a EMD program.(for EMD Nstd=0,NE=1) % This code limited sift number=10 ,the stoppage criteria can't change. % References: % Wu, Z., and N. E Huang (2008), % Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. % Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41. % % code writer: Zhaohua Wu. % footnote:S.C.Su 2009/03/04 % % There are three loops in this code coupled together. % 1.read data, find out standard deviation ,devide all data by std % 2.evaluate TNM as total IMF number--eq1. % TNM2=TNM+2,original data and residual included in TNM2 % assign 0 to TNM2 matrix % 3.Do EEMD NE times-----------loop EEMD start % 4.add noise % 5.give initial values before sift % 6.start to find an IMF------IMF loop start % 7.sift 10 times to get IMF------sift loop start and end % 8.after 10 times sift --we got IMF % 9.subtract IMF from data ,and let the residual to find next IMF by loop % 6.after having all the IMFs-------------IMF loop end % 9.after TNM IMFs ,the residual xend is over all trend % 3.Sum up NE decomposition result--------loop EEMD end % 10.Devide EEMD summation by NE,std be multiply back to data %% Association: no % this function ususally used for doing 1-D EEMD with fixed % stoppage criteria independently. % % Concerned function: extrema.m % above mentioned m file must be put together %function allmode=eemd(Y,Nstd,NE) %part1.read data, find out standard deviation ,devide all data by std xsize=length(Y); dd=1:1:xsize; Ystd=std(Y); Y=Y/Ystd; %part2.evaluate TNM as total IMF number,ssign 0 to N*TNM2 matrix TNM=fix(log2(xsize))-5; % TNM=m TNM2=TNM+2; for kk=1:1:TNM2, for ii=1:1:xsize, allmode(ii,kk)=0.0; end end %part3 Do EEMD -----EEMD loop start for iii=1:1:NE, %EEMD loop NE times EMD sum together %part4 --Add noise to original data,we have X1 for i=1:xsize, temp=randn(1,1)*Nstd; % add a random noise to Y X1(i)=Y(i)+temp; end %part4 --assign original data in the first column for jj=1:1:xsize, mode(jj,1) = Y(jj); % assign Y to column 1of mode end %part5--give initial 0to xorigin and xend xorigin = X1; % xend = xorigin; % %part6--start to find an IMF-----IMF loop start nmode = 1; while nmode <= TNM, xstart = xend; %last loop value assign to new iteration loop %xstart -loop start data iter = 1; %loop index initial value %part7--sift 10 times to get IMF---sift loop start while iter<=10, [spmax, spmin, flag]=extrema(xstart); %call function extrema %the usage of spline ,please see part11. upper= spline(spmax(:,1),spmax(:,2),dd); %upper spline bound of this sift lower= spline(spmin(:,1),spmin(:,2),dd); %lower spline bound of this sift mean_ul = (upper + lower)/2; %spline mean of upper and lower xstart = xstart - mean_ul; %extract spline mean from Xstart iter = iter +1; end %part8--subtract IMF from data ,then let the residual xend to start to find next IMF xend = xend - xstart; nmode=nmode+1; %part9--after sift 10 times,that xstart is this time IMF for jj=1:1:xsize, mode(jj,nmode) = xstart(jj); end end %part10--after gotten all(TNM) IMFs ,the residual xend is over all trend % put them in the last column for jj=1:1:xsize, mode(jj,nmode+1)=xend(jj); end %after part 10 ,original + TNM IMFs+overall trend ---those are all in mode allmode=allmode+mode; end %part3 Do EEMD -----EEMD loop end %part11--devide EEMD summation by NE,std be multiply back to data allmode=allmode/NE; allmode=allmode*Ystd;