From f0c9988d2ced1c6e7495a11292117d7d0411516b Mon Sep 17 00:00:00 2001
From: Erik Frisk <erik.frisk@liu.se>
Date: Tue, 19 Jun 2018 23:24:46 +0200
Subject: [PATCH] Slight cleanup of code and added SingleFaultIsolation.m to
src directory
---
README.md | 2 +-
code/main.m | 154 +++++++++++++++++---------------
code/src/SingleFaultIsolation.m | 14 +++
3 files changed, 97 insertions(+), 73 deletions(-)
create mode 100644 code/src/SingleFaultIsolation.m
diff --git a/README.md b/README.md
index e0625ce..d6fae27 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
This repository contains data and code to generate results from the paper
_Residual Selection for Consistency Based Diagnosis Using Machine Learning Models_
-by Erik Frisk and Mattias Krysander, Department of Electrical Engineering, Linköping Univeristy, Sweden
\ No newline at end of file
+by Erik Frisk and Mattias Krysander, Department of Electrical Engineering, Linköping University, Sweden
diff --git a/code/main.m b/code/main.m
index d86569f..844a08c 100644
--- a/code/main.m
+++ b/code/main.m
@@ -1,6 +1,6 @@
% This script produces the results shown in:
%
-% Erik Frisk and Mattias Krysander (2018). Residaul Selection for
+% Erik Frisk and Mattias Krysander (2018). Residual Selection for
% Consistency Based Diagnosis Using Machine Learning Models. In Safeprocess
% 2018, Warsaw, Polen.
%
@@ -52,47 +52,48 @@ plotResiduals(absdata)
clear absdata
%% Plot Fig. 5: Probability of residual alarm given a given mode
-no_residuals = size(data.res,2);
-all_residuals =[1:no_residuals]; % select all residuals
-plotDecisionMatrix(all_residuals,data);
+no_residuals = size(data.res, 2);
+all_residuals = 1:no_residuals; % select all residuals
+plotDecisionMatrix(all_residuals, data);
clear no_residuals ans
%% Plot Fig 6: Structural isolability of all available residuals
load model.mat;
-figure;
-model.IsolabilityAnalysisFSM(data.fsm(:,[2:8]),'permute',0);
+figure(16);
+model.IsolabilityAnalysisFSM(data.fsm(:, 2:8), 'permute', 0);
title('Structural isolability matrix using all available tests');
-ylabel('Injected fault','FontWeight', 'bold');
-xlabel('Diagnsoed fault','FontWeight', 'bold');
+ylabel('Injected fault', 'FontWeight', 'bold');
+xlabel('Diagnsoed fault', 'FontWeight', 'bold');
clear ans
+
%% Create thresholded residuals
thdata = data;
-thdata.res = abs(data.res)>=1;
+thdata.res = abs(data.res) >= 1;
%% Plot Fig 7: Confusion matrix obtained by consistency-based diagnosis (CBD) using all thresholded residuals
-figure;
-CBDPlots(thdata,model,all_residuals);
+figure(17);
+CBDPlots(thdata, model, all_residuals);
clear all_residuals ans
%% Train a random forest of 100 classification trees using the all data.
trees = 100;
rng(1); % For reproducibility
-t = templateTree('PredictorSelection','interaction-curvature');
+t = templateTree('PredictorSelection', 'interaction-curvature');
fprintf('Building random forest classifier ... ');
tic;
-Mdl = fitcensemble(single(thdata.res),thdata.mode,'Method','bag',...
- 'NumLearningCycles',trees,'Learners',t);
+Mdl = fitcensemble(single(thdata.res), thdata.mode, 'Method', 'bag',...
+ 'NumLearningCycles', trees, 'Learners', t);
fprintf(' Finished in %.2f sec.\n', toc);
clear t trees
%% Plot Fig 8: Random forest classification performance using all tests on out-of-bag data.
Y = oobPredict(Mdl);
-Ival = confusionmat(categorical(thdata.mode),categorical(Y));
+Ival = confusionmat(categorical(thdata.mode), categorical(Y));
nf = 8;
-C = Ival./(sum(Ival')'*ones(1,nf));
+C = Ival./(sum(Ival, 2)*ones(1, nf));
-figure;
+figure(18);
b = bar3(C);
title('Confusion matrix - Validation data set')
set(gca, 'XTickLabel', data.modes, 'TickLabelInterpreter', 'none')
@@ -104,8 +105,9 @@ title('Fault Isolation Performance Matrix')
for finjIdx=1:nf
for fdiagIdx=1:nf
- text(fdiagIdx,finjIdx,C(finjIdx,fdiagIdx)+0.05,...
- sprintf('%.1f',C(finjIdx,fdiagIdx)*100),'HorizontalAlignment', 'center')
+ text(fdiagIdx, finjIdx, C(finjIdx, fdiagIdx) + 0.05,...
+ sprintf('%.1f', C(finjIdx,fdiagIdx)*100), ...
+ 'HorizontalAlignment', 'center')
end
end
for k = 1:length(b)
@@ -114,15 +116,15 @@ for k = 1:length(b)
b(k).FaceColor = 'interp';
end
colormap('Summer')
-view(0,90)
+view(0, 90)
clear Y Ival nf C b fdiagIdx finjIdx k zdata thdata
%% Plot Fig 9: Random forest out-of-bag classification error as a function of the number of trees.
-figure;
-oobErr = oobLoss(Mdl,'mode','cumulative');
-plot(oobErr,'LineWidth',2)
-xlabel( 'Number of grown trees' , 'FontWeight', 'bold');
-ylabel( 'Out-of-bag classification error' , 'FontWeight', 'bold');
+figure(19);
+oobErr = oobLoss(Mdl, 'mode', 'cumulative');
+plot(oobErr, 'LineWidth', 2)
+xlabel('Number of grown trees', 'FontWeight', 'bold');
+ylabel('Out-of-bag classification error', 'FontWeight', 'bold');
clear oobErr
%% Estimate residual importance by permuting out-of-bag observations.
@@ -132,13 +134,14 @@ importance = oobPermutedPredictorImportance(Mdl);
fprintf(' Finished in %.2f sec.\n', toc);
%% Plot Fig 10: Residual importance
-[imp,residualRanking] = sort(importance,'descend');
+[imp,residualRanking] = sort(importance, 'descend');
-figure;
-plot(imp,'LineWidth',2)
-xticks([1:length(residualRanking)])
+figure(20);
+plot(imp,'LineWidth', 2)
+xticks(1:length(residualRanking))
axis([0 43 -0.4 1.6])
-set(gca, 'XTickLabel', residualRanking, 'TickLabelInterpreter', 'none', 'FontSize',8)
+set(gca, 'XTickLabel', residualRanking, 'TickLabelInterpreter', 'none', ...
+ 'FontSize', 8)
title('Out-of-Bag Permuted Predictor Importance Estimates');
ylabel('Estimates','FontWeight', 'bold');
xlabel('Predictors','FontWeight', 'bold');
@@ -146,80 +149,87 @@ clear imp
%% Performance evaluation of selected tests using consistency based diagnosis.
-no_samples = size(data.res,1); % n
+no_samples = size(data.res, 1); % n
no_modes = numel(data.modes); % m
no_of_samples_per_mode = no_samples/no_modes; % k
-structuralIsolationMatrix = model.IsolabilityAnalysisFSM(data.fsm(:,[2:8]),'permute',0);
+structuralIsolationMatrix = ...
+ model.IsolabilityAnalysisFSM(data.fsm(:, 2:8),'permute', 0);
+
+stim2 = sum(((2.^(0:6)).*structuralIsolationMatrix), 2);
+probabilityMaximumIsolation = zeros(42, no_modes-1);
-stim2 = sum(((2.^[0:6]).*structuralIsolationMatrix)');
-probabilityMaximumIsolation = zeros(42,no_modes-1);
-C ={};
+C = cell(length(residualRanking), 1);
+isolationerror = zeros(length(residualRanking), 1);
+mean_md = zeros(length(residualRanking), 1);
+fa = zeros(length(residualRanking), 1);
for i = 1:length(residualRanking)
- selected_residauls = residualRanking(1:i); % select the i best residuals
- dx = SingleFaultIsolation(data.res(:,selected_residauls),...
- data.fsm(selected_residauls,[2:8])); % apply CBD.
+ selected_residuals = residualRanking(1:i); % select the i best residuals
+ dx = SingleFaultIsolation(data.res(:, selected_residuals),...
+ data.fsm(selected_residuals, (2:8))); % apply CBD.
% Compute for each mode the probability of maximum isolation
- dx2 = sum(((2.^[0:6]).*dx(:,[2:end]))');
+ dx2 = sum(((2.^(0:6)).*dx(:, (2:end))), 2);
for j = 1:no_modes-1 % not NF
- probabilityMaximumIsolation(i,j) = sum(dx2([1:no_of_samples_per_mode]+...
+ probabilityMaximumIsolation(i,j) = sum(dx2((1:no_of_samples_per_mode)+...
j*no_of_samples_per_mode)==stim2(j))/no_of_samples_per_mode;
end
% Compute confusion matrix of CBD using the i best residuals
- C{i} = CBDPlots(data,model,selected_residauls);
+ C{i} = CBDPlots(data, model, selected_residuals);
% Compute performance indicators
isolationerror(i) = sum(sum(abs(structuralIsolationMatrix-C{i}(2:8,2:8)./((1-C{i}(2:end,1))*ones(1,7)))))/7^2;
- mean_md(i) = mean(C{i}(2:end,1));
- fa(i) = 1-C{i}(1,1);
+ mean_md(i) = mean(C{i}(2:end, 1));
+ fa(i) = 1-C{i}(1, 1);
end
-clear stim2 dx dx2 no_samples no_modes no_of_samples_per_mode selected_residauls C
+clear stim2 dx dx2 no_samples no_modes no_of_samples_per_mode selected_residuals C
clear structuralIsolationMatrix i j
%% Plot Fig 11: False alarm probability, mean missed detection probability,
% and aggregated isolation error as a function of selected tests.
-figure;
-plot([mean_md;fa;isolationerror]','LineWidth',2)
+figure(21);
+plot([mean_md, fa, isolationerror], 'LineWidth', 2)
hold
-selected_no_of_residauls = [12 14 26 27];
-plot(selected_no_of_residauls,isolationerror(selected_no_of_residauls),'x',...
- 'LineWidth',2,'MarkerSize',8,'Color','k')
+selected_no_of_residuals = [12 14 26 27];
+plot(selected_no_of_residuals,isolationerror(selected_no_of_residuals), ...
+ 'x', 'LineWidth', 2, 'MarkerSize', 8, 'Color', 'k')
-legend('Missed detection probability','False alarm probability','Fault isolation errors')
-xlabel('Number of selected residauls','FontWeight', 'bold')
-ylabel('Probability','FontWeight', 'bold')
+legend('Missed detection probability', 'False alarm probability', ...
+ 'Fault isolation errors')
+xlabel('Number of selected residuals','FontWeight', 'bold')
+ylabel('Probability', 'FontWeight', 'bold')
grid on
-clear mean_md isolationerror selected_no_of_residauls
+clear mean_md isolationerror selected_no_of_residuals
%% Plot Fig 12: Consistency based isolation performance per mode as a function of number of selected residuals.
-figure;
-plot([1-fa; probabilityMaximumIsolation']','LineWidth',2)
+figure(22);
+plot([1-fa, probabilityMaximumIsolation], 'LineWidth', 2)
l = legend(data.modes{1:end});
set(l, 'Interpreter', 'none')
-xlabel('Number of selected residuals','FontWeight', 'bold')
-ylabel('Probability of maximum isolation performance','FontWeight', 'bold')
+xlabel('Number of selected residuals', 'FontWeight', 'bold')
+ylabel('Probability of maximum isolation performance', 'FontWeight', 'bold')
grid on
clear probabilityMaximumIsolation fa l
%% Plot Fig 13: Consistency-based diagnosis results obtained when running different numbers k of most important residuals.
% Plots the cases when using 10, 12, 26, and 27 residuals:
-selected_no_of_residauls = [10 12 26 27];
-figure;
-C ={};
-for i = 1:length(selected_no_of_residauls)
- subplot(2,2,i);
- selected_residauls = residualRanking(1:selected_no_of_residauls(i));
- C{i} = CBDPlots(data,model,selected_residauls);
- title(['No of tests: ' num2str(selected_no_of_residauls(i))]);
+selected_no_of_residuals = [10 12 26 27];
+figure(23);
+C = cell(length(selected_no_of_residuals), 1);
+for i = 1:length(selected_no_of_residuals)
+ subplot(2, 2, i);
+ selected_residuals = residualRanking(1:selected_no_of_residuals(i));
+ C{i} = CBDPlots(data, model, selected_residuals);
+ title(['No of tests: ' num2str(selected_no_of_residuals(i))]);
end
clear C i
%% Plot structural isolability using the k best residuals. Compare with previous plot.
-figure;
-for i = 1:length(selected_no_of_residauls)
- subplot(2,2,i)
- selected_residauls = residualRanking(1:selected_no_of_residauls(i));
- model.IsolabilityAnalysisFSM(data.fsm(selected_residauls,[2:8]),'permute',0); % full str isolability
- title(['No of tests: ' num2str(selected_no_of_residauls(i))])
+figure(24);
+for i = 1:length(selected_no_of_residuals)
+ subplot(2, 2, i)
+ selected_residuals = residualRanking(1:selected_no_of_residuals(i));
+ model.IsolabilityAnalysisFSM(data.fsm(selected_residuals, (2:8)), ...
+ 'permute', 0); % full str isolability
+ title(['No of tests: ' num2str(selected_no_of_residuals(i))])
end
-%clear selected_no_of_residauls selected_residauls residualRanking i ans
+%clear selected_no_of_residuals selected_residuals residualRanking i ans
diff --git a/code/src/SingleFaultIsolation.m b/code/src/SingleFaultIsolation.m
new file mode 100644
index 0000000..7673f5a
--- /dev/null
+++ b/code/src/SingleFaultIsolation.m
@@ -0,0 +1,14 @@
+function isol=SingleFaultIsolation( res, FSM )
+ N = size(res,1);
+ nf = size(FSM,2);
+
+ isol = zeros(N,nf + 1);
+ for k=1:N
+ alarms = abs(res(k,:))>=1;
+ if any(alarms)
+ isol(k,:) = [false all(FSM(alarms,:),1)];
+ else
+ isol(k,:) = [true boolean(zeros(1,nf))];
+ end
+ end
+end
\ No newline at end of file
--
GitLab