When you select either of the import methods, the app selects the variables to display based on format, not content. The list of candidate datasets are therefore similar for both methods. The app bases its interpretation of the dataset on the import method you select.

For more information about the import dialog boxes, see:

For more information about terms related to data ensembles, see More About.

For more information about organizing your data for import into the app, see Organize System Data for Diagnostic Feature Designer.

Specify preferences for how Diagnostic Feature Designer performs computations and where the app stores results. Computation Options opens a dialog box that accepts these preferences. Some options are visible only under specific conditions:

For more information, see Computation Options.

Select options for processing your data into new signals. Use these new signals as inputs to other processing options or as inputs to feature generation. Most processing options operate on each ensemble member. You can also perform ensemble-level processing to view how the ensemble behaves as a whole. Each option selection opens a dialog box for your specifications.

For information about the choices, see:

Compute features in the time domain. Signal Features apply to any signals. Rotational Machinery Features are specialized metrics that apply to gearing. Nonlinear Features provide metrics that characterize chaotic behavior in vibration signals. Each selection opens a dialog box for your specifications. For more information, see:

Spectral features provide frequency-domain metrics on your data. To compute spectral features, you must already have a power spectrum or an order spectrum variable. SelectingSpectral Features opens a dialog box for your specification. For more information, see Spectral Features.

Open the feature ranking tab that lets you perform classification and prognostic ranking for the feature table that you select. For more information, see Feature Ranking Tab.

Export features, or your entire data set, to use them or share them outside of the app. Generate code to reproduce your feature computations in a MATLAB function.

For more information about the Export options, see:

Use the Panner to focus on data segments in the x-axis range that you specify and to change the plot scale. The Panner provides a strip plot beneath the main plot. To focus on a section of the main plot, move the handles. To change the scale of the plot, select one of the options in Scale.

Use Ensemble View Preferences to control how you view your data as an ensemble:

Specify how to plot multiple variables together.

In a signal or spectrum plot, you highlight an individual member by positioning your cursor on the member trace. Select Show Signal Information to display both the variable member that you highlight and the condition label for that member in the lower right corner.

Select Data Cursors to display values of key points in your signal. Data Cursors are horizontal and vertical bars that you position over a point of interest, such as a peak value. The cursors display the x and y positions. To display the distance between the cursors, select Show Signal Information. To lock the bars so that they move together, select one of the Lock Spacing options.

Click Select Features to open a selectable list of features to plot. Use Select Features, for example, when you have generated many features but you want to focus on a subset in a single plot panel.

Select the condition variable to base feature histograms on. The feature histograms use color to visualize the separation of data groups with different labels for that condition variable.

Example: faultCode

Specify histogram resolution, as driven by your selection of Bin Width, Bin Method, Number of Bins, and Bin Limits. The bin settings apply to all the histograms for the feature table

The bin settings are not independent. The algorithm uses an order of precedence to determine what to use:

Select a classification ranking technique to assess how effectively each feature separates data with different condition labels. If you have already ranked your features, you can rank again with a different technique and display the resulting rankings together. Each technique uses a different statistical method.

The menu differentiates between two-class and multiclass ranking methods.

The default ranking technique for two-class condition variables, t-test, is the simplest technique, as it uses only the means of the two labeled groups and not their distributions. t-test is primarily useful for identifying ineffective features to discard.

The table lays out the influence of specific criteria on ranking-method selection.

Selecting a technique activates a new tab with a name that matches the ranking technique. For more information on this technique-activated tab, see Ranking Technique Tab.

For more information on the ranking methods, see:

Select a prognostic ranking technique to assess how effectively each feature tracks the degradation of your ensemble members when you have run-to-failure data. The top-ranking features are best at predicting the remaining useful life (RUL).

The app provides three prognostic ranking techniques, all of which score features on a scale ranging from 0 through 1. One technique, Monotonicity, is always available. The other two techniques, Trendability and Prognosability, are available only when you are using frame-based data. The smaller data segments in frame-based data allow the tracking of small changes that are induced by degradation.

Selecting a technique activates a new tab with a name that matches the ranking technique. For more information on this technique-activated tab, see Ranking Technique Tab.

For an example of using prognostic ranking in the app, see Perform Prognostic Feature Ranking for a Degrading System Using Diagnostic Feature Designer.

Select the condition variable that provides the labels for the classification ranking algorithm to use.

Specify the ranking technique to sort by when you are comparing the results of different ranking methods. When you use a single ranking technique, the app displays the results in order of importance, as indicated by the ranking score for that technique. When you are comparing the results for multiple methods, change Sort By to change the technique that drives the sorting order.

Specify this parameter to eliminate ranking scores for a specific technique. Use this parameter, for example, when you are comparing the results of multiple rankings, and you want to simplify the display by eliminating rankings that do not influence your feature selection.

Export features to use them or share them outside of the app. Both options open a ranking-sorted selectable list to choose from. When you export to the MATLAB workspace, you can use command-line techniques with the features. When you export to the Classification Learner, you open a Classification Learner session that uses your selected features as input.

If you want to export your entire data set from the app, use Export from the Feature Designer tab.

You can also generate code that reproduces the computations for the variables and features you select. For more information, see the code generation options description in the Export section in the Feature Designer tab. When you generate code using Generate Function for Features from the Feature Ranking tab, Ranking Method defaults to the method you specify in Sort By.

The correlation importance setting allows you to screen out features that convey similar information to higher-ranked features. This screening provides a more diverse feature set in the upper ranks.

The criterion for the screening is the set of cross-correlation coefficients a feature has with higher-ranked features. High cross-correlation between two features implies that both features are separating condition groups similarly and provide redundant information. With the default value of 0, the app does not incorporate feature redundancy into ranking scores. As you increase the correlation importance value, the app increases the influence of feature cross-correlation on the feature ranking score. This increasing influence progressively lowers the score of redundant features.

The normalization scheme performs independent normalization across the members for every feature. Normalization allows more direct comparisons among features. The app displays the defining equation for the scheme you select directly beneath your selection.

This option is available only for classification ranking techniques.

Click Apply to calculate ranking with the specified parameters. The Feature Ranking tab in the plotting area displays the results both graphically and tabularly. This display also includes the results for the default ranking algorithm, and for any other ranking techniques you calculated previously.

Once you calculate a ranking, the app disables Apply until you change a parameter. You can calculate ranking within a tab multiple times. Each time you modify the parameters and calculate ranking, the new results overwrite the previous results in the plotting-area tab.

Once you have completed your ranking within the ranking technique tab, close that tab to return control to the Feature Ranking tab. The Feature Ranking is disabled while any ranking technique tab is activated.

This property is read-only.

The frame policy information reflects the choice you make when you select Export > Generate Function for... in the Feature Designer tab.

Set criteria to refine your options when selecting items for your generated function. All criteria allow you to overwrite selectable options with a string. String matching is case insensitive. Your filters apply to all output items, including signals, features, and ranking tables. Criteria include:

To reset a single filter, delete the contents and click anywhere in the app. To reset all filters at once, click Reset Filters.

Display all selected items together. Use Sort Selected especially when you have used multiple filter combinations to assemble your codegen selections. All your selections appear together.

Click the Generate Function button when you have completed configuring your selections. The app opens a function that contains computations used for all the output items you selected.

For more information about generating code in the app, see Automatic Feature Extraction Using Generated MATLAB Code.