多尺度熵特征.zip_信号特征提取_多尺度熵_多尺度特征熵_机械振动_机械故障诊断

function E=MPE(iSig,m,s) % iSig: input signal; m : Embedded dimension; % s: scale number for i=1:1:s %i : scale index oSig=CoarseGrain(iSig,i); E(i)=PE(oSig,m); end %Coarse Grain Procedure. See Equation (11) % iSig: input signal ; s : scale numbers ; oSig: output function oSig=CoarseGrain(iSig,s) N=length(iSig); %length of input signal for i=1:1:N/s oSig(i)=mean(iSig((i-1)*s+1:i*s)); end % function to calculate permutation entropy % signal: input signal; m: embedded dimension function E=PE(sig,m) N=length(sig); %length of signal v=[1:m]; % m=3, v=[1 2 3]; m=5, v=[1 2 3 4 5] all_pemu=perms(v); % generate all possible permutations perm_num=factorial(m); % calculate m! to obtain the number of all possible permutations for i=1:1:perm_num key(i)=genkey(all_pemu(i,:)); %transform a vector into an integer; ex: [4 3 1 2] ==> 4321 end pdf=zeros(1,perm_num); %initialize frequency array for i=1:1:N-m+1 pattern=sig(i:i+m-1); % obtain pattern vector from signal. [Y,order]=sort(pattern); % sort the pattern vector; order represents the permutation order. pkey=genkey(order); %transform the order vector into an integer. id=find(key==pkey); pdf(id)=pdf(id)+1; end pdf=pdf/(N-m+1); % normalize the frequency array to obtain probability density function. %calculate the entropy E=0; for i=1:1:perm_num if (pdf(i)~=0) E=E-pdf(i)*log(pdf(i)); %calculate entropy. end end perm_num = min(perm_num, N-m+1); E=E/log(perm_num); %normalize entropy. %function to transform a vector into an integer; ex: [2 1 3]==> 213, [4 3 1 2] ==> 4321 function key=genkey(x) key=0; for i=1:1:length(x) key=key*10+x(i); end