PCA Dimensionality Reduction of Main Engine Sensor Data for Ship Condition Monitoring

Project info

• Field of studyNaval Architecture and Ocean Engineering
• LevelUndergraduate thesis (S1)
• Method typeMachine Learning
• Estimated build time± 3 working days
• Toolspca, zscore, biplot (Statistics and Machine Learning Toolbox)
• PriceUS$19 - US$116

Research goal

Predict engine condition principal component score from exhaust gas temperature and lubricating oil pressure using PCA (pca) and assess model performance.

Variables

Example result

• 59.065PC1 variance (%)
• 27.214PC2 variance (%)
• 86.279PC1+PC2 (%)
• 3Components for 90%
• 2.953Eigenvalue PC1
• 1.361Eigenvalue PC2

Two principal components explain 86.3% of the variance (PC1 59.1%, PC2 27.2%) across five variables including Engine condition principal component score, Exhaust gas temperature and Lubricating oil pressure; 3 components are needed to reach 90%.

Computed from illustrative synthetic data, not from a real study.

About PCA (pca)

pca reduces dimensionality by transforming correlated variables into uncorrelated principal components ordered by variance. It is used to summarise many variables, reduce multicollinearity, and visualise multivariate data.

Steps

1. 1Standardise the data with zscore
2. 2Run pca and obtain coefficients, scores, and variances
3. 3Inspect percent variance explained and the scree plot
4. 4Choose the number of components (for example 90 percent cumulative)
5. 5Interpret loadings to name the components
6. 6Plot the scores or a biplot

Assumptions to check

• Variables are on comparable scales after standardisation
• Relations among variables are roughly linear
• Large variance is assumed to carry information

Reading and reporting the results

Report the variance explained by each component, the number retained, and the main loadings. Name components by their dominant variables, taking care not to over-interpret.

Example MATLAB code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

rng(9);
n = 300;
f1 = randn(n, 1);  f2 = randn(n, 1);
exhaust_gas_temperature = f1 + 0.3*randn(n, 1);
lubricating_oil_pressure = f1 + 0.3*randn(n, 1);
x3 = f2 + 0.3*randn(n, 1);
x4 = f2 + 0.3*randn(n, 1);
x5 = randn(n, 1);
engine_condition_principal_c = [exhaust_gas_temperature lubricating_oil_pressure x3 x4 x5];

Z = zscore(engine_condition_principal_c);
[coeff, score, latent, ~, explained] = pca(Z);
cum = cumsum(explained);
fprintf('Explained (%%): %s\n', mat2str(explained', 3));
fprintf('Cumulative (%%): %s\n', mat2str(cum', 3));
k = find(cum >= 90, 1);
fprintf('Components for 90%%: %d\n', k);
disp(array2table(coeff(:, 1:2), 'VariableNames', {'PC1', 'PC2'}, ...
    'RowNames', {'x1', 'x2', 'x3', 'x4', 'x5'}));

figure;
subplot(1,2,1); plot(explained, '-o'); xlabel('Component'); ylabel('Variance explained (%)');
subplot(1,2,2); biplot(coeff(:, 1:2), 'Scores', score(:, 1:2), 'VarLabels', {'x1', 'x2', 'x3', 'x4', 'x5'});

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions