# Examples of Data Analytics for Predictive Maintenance (1)

## Contents

## Method1: Hotelling's T-square Method

Among many statistical anomaly detection techniques, Hotelling’s T-square method, a multivariate statistical analysis technique, has been one of the most typical method. This method has a fundamental assumption that the data follow a unimodal distribution. Based on this assumption, the method calculates squared Mahalanobis distance for each data in multi-dimensional space, and judges x percent outliers in dataset as an anomaly.

## Load the Pre-processed Data

This data is generated by running Demo0_PreProcessing.m.

```
load('Preprocessed_FD001.mat');
```

## Hotelling's T-square Test

To visualize results, I would like to apply Hotelling's T-square method to the first two PCA components.

First, the group of data points labeled 'long' is assumed as a normal condition. Since the total number of explanatory variables (= 2) are much smaller than that of data samples (= 20631), the distribution of Mahalanobis distance can be approximated by the chi-squared distribution (degree of freedom = 2).

Next, the thresholds to detect 5%, 1% and 0.1% outliers are calculated based on this chi-squared distribution.

Finally, I would like to create a plot of the first two components with these thresholds.

% Calculate the thresholds of Mahalanobis distance to detect 5%, 1% and % 0.1% outliers idx = (dataTrainZ.Label == 'long'); th = [0.001, 0.01, 0.05]; C = cell(3,1); % Calculate the Mahalanobis distance for each data d = 0.1; [x1Grid, x2Grid] = meshgrid(-8:d:8, -8:d:8); aGrid = mahal([x1Grid(:) x2Grid(:)], score(idx,1:2)); aGrid = reshape(aGrid, size(x1Grid,1), size(x2Grid,2)); % Calculate the thresholds to detect 5%, 1% and 0.1% outliers on the the % first two components for kk=1:3 lev = chi2inv((1-th(kk)),2); C{kk} = contourc((-8:d:8), (-8:d:8), aGrid, [lev lev]); end % Set the color for 5%, 1% and 0.1% outlier regions col5per = [0.75 0.95 1]; col1per = [0.75 1 0.75]; col01per = [1 1 0.75]; colAnomaly = [1 0.85 0.85]; % Plot the result figure plotContourmatrix(C{1}, col01per); plotContourmatrix(C{2}, col1per); plotContourmatrix(C{3}, col5per); hold on; s1 = gscatter(score(:,1),... score(:,2),... dataTrainZ.Label); idx = dataTrainZ.Time == 0; s2 = plot(score(idx,1),... score(idx,2),'rp','MarkerSize',10,'MarkerFaceColor','w'); legend([s1; s2],{'urgent','short','medium','long','failure occurrence'},... 'Color', [1 1 1],... 'Location', 'northwest',... 'FontSize', 12); ax = gca; ax.Color = colAnomaly; ax.Box = 'on'; ax.Layer = 'top'; xlabel('1st Principal Component','FontSize',12); ylabel('2nd Principal Component','FontSize',12); title({'Training Data and Calculated Outlier Detection Threshold';... 'Blue: Normal, Green: 5% outlier, Yellow: 1% outlier, Red: 0.1% outlier'},'FontSize',12)

## Applying to the Test Set

In this process, these thresholds (which can detect 5%, 1% and 0.1% outliers) are applied to the test data set. The result is shown by creating a plot of the first two components with test set and these thresholds. In this plot, conditions of each engine are shown by changing colors for each data point. In real situaiton, only the positions of each data point and the thresholds for outlier detection are available.

As shown in this plot, the thresholds for outlier detectio calcuated by the Hotelling's T-square method is quite helpful to detect anomalies and foresee machine failure during normal operation. In this example, all the 'urgent' condition in the test set can be detected by using the threshold for 1% outlier detection.

% Standardize the test set dataTestZ = dataTest; dataTestZ{:,3:end-1} = (dataTest{:,3:end-1} - mu)./sigma; % Convert the sensor data into PCA components score = dataTestZ{:,3:end-1}*wcoeff; % Plot the result figure plotContourmatrix(C{1}, col01per); plotContourmatrix(C{2}, col1per); plotContourmatrix(C{3}, col5per); hold on; s1 = gscatter(score(:,1),... score(:,2),... dataTestZ.Label); legend(s1,{s1.DisplayName},... 'Color', [1 1 1],... 'Location', 'northwest',... 'FontSize', 12); ax = gca; ax.Color = colAnomaly; ax.Box = 'on'; ax.Layer = 'top'; xlabel('1st Principal Component','FontSize',12); ylabel('2nd Principal Component','FontSize',12); title({'Applying Outlier Detection Threshold for the Test Data';... 'Blue: Normal, Green: 5% outlier, Yellow: 1% outlier, Red: 0.1% outlier'},'FontSize',12)