Function: exampleDLAR

2017-01-16  by:CAE仿真在線  來(lái)源:互聯(lián)網(wǎng)

Function: exampleDLAR

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% 該程序由原作者免費(fèi)共享提供,不作商業(yè)用途的前提下,本人才得以拿出來(lái)供同行

% 及對(duì)matlab編程有興趣的朋友分享、共同學(xué)習(xí)。


% AUTHORS:

% Eloi Figueiredo

%

% LA-CC-10-032

% Copyright (c) 2010, Los Alamos National Security, LLC

% All rights reserved.


% *******

%% Load Raw Data


%%

% Load data set:

load('data3SS.mat'); %導(dǎo)入數(shù)據(jù)文件

% 原數(shù)據(jù)格式為8192個(gè)樣本點(diǎn),5個(gè)通道,第1通道為激勵(lì),第2至5通道為響應(yīng);

% 數(shù)據(jù)第3維為測(cè)量次數(shù),共170次,

data=dataset(:,2:5,:); %也就是說(shuō)本程序使用的數(shù)據(jù)為全部170次測(cè)量的第2至5通道響應(yīng)數(shù)據(jù)



% 以下代碼為將第1次測(cè)量的響應(yīng)數(shù)據(jù)4個(gè)通道畫(huà)在圖中。效果如下Figure 1

% Plot time histories from the baseline condition (Channel 2-5):


figure


for i=1:4;


subplot(2,2,i)

plot(data(:,i,1),'k')

title(['Channel ',num2str(i+1)])

set(gca,'YTick',-2:2,'Xlim',[1 8192],'Ylim',[-2.5 2.5])


if i==3 || i==4, xlabel('Observations'); end

if i==1 || i==3, ylabel('Acceleration (g)'); end


end


%確定AR模型階數(shù)為15,這里選擇了確定的數(shù),實(shí)際在處理未知數(shù)據(jù)時(shí)需要進(jìn)行試算確定

% AR model order:

arOrder=15;


%% 估計(jì)AR模型參數(shù),就是AR系數(shù),下面程序?qū)⒂?jì)算出170次測(cè)量數(shù)據(jù)的AR系數(shù)

% 4個(gè)通道,170次,每個(gè)響應(yīng)的AR系數(shù)有15個(gè),最后形成的矩陣為170*60

% Estimation of the AR Parameters:

[arParameters]=arModel_shm(data,arOrder);


[n m]=size(arParameters); % n = 170, m = 60;


% 畫(huà)出AR系數(shù)圖,并將未損用黑色表示,損傷用紅色表示,如Figure 2

% Plot test data:

figure;

plot(1:m,arParameters(1:90,:)','k',1:m,arParameters(90:170,:)','r')

title(['Concatenated AR(',num2str(arOrder),') Parameters for all Instances'])

legend('Undamaged','Damaged')

xlabel('AR Parameters')

ylabel('Amplitude')

w=8;

set(gca,'Xlim',[1 m])

M(1,1:9)='Undamaged'; M(2,1:7)='Damaged';

legend([line('color','k');line('color','r')],M);

h(1)=line([m/4;m/4],[-w w],'color','k','lineStyle','-.');

h(2)=line([m/4*2;m/4*2],[-w w],'color','k','lineStyle','-.');

h(3)=line([m/4*3;m/4*3],[-w w],'color','k','lineStyle','-.');

text(4,-7,'Channel 2','Color','k','EdgeColor','k','BackgroundColor','w')

text(18,-7,'Channel 3','Color','k','EdgeColor','k','BackgroundColor','w')

text(33,-7,'Channel 4','Color','k','EdgeColor','k','BackgroundColor','w')

text(48,-7,'Channel 5','Color','k','EdgeColor','k','BackgroundColor','w')


% 以下特征提取方法與診斷過(guò)程參考文獻(xiàn)[1]

% 檢測(cè)方法原理如下

%

% 1. 訓(xùn)練樣本取自結(jié)構(gòu)健康狀態(tài)的特征數(shù)據(jù),計(jì)算其均值 與協(xié)方差矩陣

% 2. 測(cè)試樣本取自結(jié)構(gòu)任意狀態(tài)的特征數(shù)據(jù),計(jì)算其到訓(xùn)練樣本的Mahalanobis平方距離

%Function: exampleDLAR


%% Data Normalization for Novelty Detection

% The Mahalanobis-based machine learning algorithm is used to normalize the

% features and reduce each feature vector to a score.


DI=zeros(17,4);

scoreData=zeros(17,arOrder);


cnt=1;


for i=1:4;

% 取前9個(gè)健康工況第1至9次測(cè)量的AR系數(shù)作為訓(xùn)練樣本,每個(gè)工況的第10次作為測(cè)試樣本

for j=1:9;

learnData(j*9-8:j*9,:)=arParameters(j*10-9:j*10-1,cnt:cnt+arOrder-1);

end


scoreData(1:17,:)=arParameters(10*(1:17),cnt:cnt+arOrder-1);

% 9個(gè)工況的前9次測(cè)量的樣本進(jìn)行訓(xùn)練,獲得模型model

[model]=learnMahalanobis_shm(learnData);

% 用model計(jì)算17個(gè)工況第10次測(cè)量的測(cè)試樣本,

DI(:,i)=scoreMahalanobis_shm(scoreData,model);


cnt=cnt+arOrder;


end

%取指標(biāo)的相反數(shù)

DI=-DI;


% 作圖,如Figure 3

%% Damage Location


%%

% Plot DIs from Channel 2-5:

figure


for i=1:4;


subplot(2,2,i)

bar(1:9,DI(1:9,i),'k'); hold on; bar(10:17,DI(10:17,i),'r')

title(['Channel ',num2str(i+1)])

set(gca,'Xlim',[0 length(DI)+1],'XTick',1:length(DI))

grid on


if i==3 || i==4, xlabel('State Condition'); end

if i==1 || i==3, ylabel('DI'); end


end


Function: exampleDLAR

Figure1


Function: exampleDLAR

Figure2



Function: exampleDLAR

Figure3


%%

% The figure above shows that the extracted features from Channels 2-3 are

% lesser sensitive than from Channels 4 and 5 to discriminate the undamaged

% (1-9) and damaged (10-17) state conditions. This is an indication that

% the source of damage is located near to Channels 4 and 5.

%%

% See also:

%

% <exampleDLARX.html Damage Location using ARX Parameters from an Array of

% Sensors>


參考文獻(xiàn)


[1] Worden, K., Manson, G., Fieller, N. R. J. Damage detection using outlier analysis[J]. Journal of Sound and Vibration, 2000, 229(3): 647-667.




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