www.gusucode.com > 基于粒子滤波的故障检测,使用似然函数作为检测函数 > 基于粒子滤波的故障检测,使用似然函数作为检测函数/code/FDI based on SIR likelihood/missPF.m

    close all;
clear all;
n = 1:600;%sample steps
stdw = sqrt(10);
ngrid = 50;
npar = 500;%particle number
N = length(n);%N表示时间点个数
A=50;F=300;
missalarm=zeros(1,9);
H=0.1:0.1:0.9;
j=0;
%N=500
% Generate the state process and observation
for h=0.1:0.1:0.9   %设定阈值，每个阈值进行仿真50次
    faultnumber=0;
    for s=1:50    %对于每个阈值仿真50次，以便计算误报率和漏报率
x0 = 0.1;
c0 = 1;
b0=25;
xpath = zeros(1,N);
xmean=0;xvariance=0.1;
ymean=0;yvariance=1;
xnoise=gauss(xmean,xvariance,N); 
ynoise=gauss(ymean,yvariance,N); 

b=b0;
xpath(1) = x0/2 + b*x0/(1+x0^2) +8*cos(0) +xnoise(1) ;
for i=2:N
    if i<301
        b=b0;
    else 
        b=b0*5;
    end
xpath(i) = xpath(i-1)/2 + b*xpath(i-1)/(1+xpath(i-1)^2) +8*cos(1.2*(i-1)) + xnoise(i);
end
zpath = 1/20*(xpath.^2) + ynoise;
% Particle filter with resampling
w = ones(npar,1)/npar;
xprev = randn(npar, 1);
SParMat = zeros(npar, N);
SXParMat = zeros(npar, N);
sxparpath = zeros(1,N);
likelihood=zeros(N,1);
D=zeros(N,1);
for i=1:N
xnext = drawpar(xprev, stdw, i);
xs = (xnext.^2)/20;
w = w.*(1/sqrt(2*pi)*exp(-((zpath(i)-xs).^2)/2));
l=1/sqrt(2*pi)*exp(-((zpath(i)-xs).^2)/2);
Li=sum(l)/npar;%i时刻的所有粒子似然函数值均值
likelihood(i)=Li;
Di=0;
if i<20
    for j=1:i
        Di=Di+(-(log(likelihood(j))));
    end
else 
    for j=i-20+1:i
        Di=Di+(-(log(likelihood(j))));
    end
end
D(i)=Di;
while i>301
 if D(i)<h
     faultnumber=faultnumber+1;
 else 
     faultnumber=faultnumber;
 end
end
w = w/sum(w);
SParMat(:,i) = w;
SXParMat(:,i) = xnext;
sxparpath(i) = w'*xnext;
[xprev, w] = impResample(xnext, w);
end
    end

    missalarm(j)=faultnumber/(A*F);%故障漏报率A是仿真总次数F是一次仿真中有故障时的时间点总个数
j=j+1;
end


%figure1;

%plot(D); 
%figure2;
plot(H,missalarm,'-');