Automate Feature Extraction Using MATLAB Code from the App

Generate MATLAB^® code in the app that reproduces the computations for your selected features, processed variables, and ranking

The Diagnostic Feature Designer app lets you work with ensemble data interactively and experiment with various processing and feature options. Once you have determined which features work best, you can generate code that reproduces your computations. This code allows you to apply the same computations to new or expanded ensemble data. You can use this code directly, or modify the code to suit your application. For more information on code generation in the app, see Automatic Feature Extraction Using Generated MATLAB Code.

Apps

Diagnostic Feature Designer

Interactively extract, visualize, and rank features from measured or simulated data for machine diagnostics and prognostics

Functions

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Workspace Ensembles

`workspaceEnsemble`	Manage ensemble data stored in the MATLAB workspace using code generated by Diagnostic Feature Designer
`findIndex`	Find the workspace ensemble member indices for members that match a specified variable name and value
`readMember`	Return ensemble member data based on the member index
`refresh`	Update a workspace ensemble with partitions of modified or added data computed in parallel processing
`writeMember`	Write data to a specific workspace ensemble member

General Ensembles

`readall`	Read all data in datastore
`readFeatureTable`	Read feature values, independent variables, and condition variables from an ensemble data set into a table
`readMemberData`	Extract data from an ensemble member given a path
`reset`	Reset datastore to initial state
`unique`	Unique values in array
`writeToLastMemberRead`	Write data to member of an ensemble datastore

Frame-Based Processing

`frameintervals`	Create frame intervals based on frame settings
`joindata`	Merge two frame tables using an outer join
`readFrameIntervals`	Extract frame segments from an ensemble member

Time and Frequency

`effectivefs`	Effective sampling rate of a time vector
`time2num`	Convert `duration` or `datetime` array into numeric vector with the specified time unit

Classification Feature Ranking

`anova1`	One-way analysis of variance
`bhattacharyyaDistance`	One-dimensional Bhattacharyya distance between two independent data groups to measure class separability
`kruskalwallis`	Kruskal-Wallis test
`perfcurve`	Receiver operating characteristic (ROC) curve or other performance curve for classifier output
`ranksum`	Wilcoxon rank sum test
`relativeEntropy`	One-dimensional Kullback-Leibler divergence of two independent data groups to measure class separability
`ttest2`	Two-sample t-test
`correlationWeightedScore`	Adjust feature ranking scores using correlation factor

Topics

Automatic Feature Extraction Using Generated MATLAB Code
Diagnostic Feature Designer can generate code that reproduces your interactive computations and allows you to automate feature extraction on similar input data. You select among your features, computed variables, and ranking tables to specify what the code includes.
Generate a MATLAB Function in Diagnostic Feature Designer
This example shows how to extract features from measurement data in the app and generate a MATLAB function that reproduces the calculations for those features.
Apply Generated MATLAB Function to Expanded Data Set
This example shows how use a reduced data set to develop features in Diagnostic Feature Designer, generate code to automate the feature computations you select, and then use that code with the full-member data set to compute a feature set for model training.
Export Feature Extraction Function and Simulink Model for Streaming Data
From Diagnostic Feature Designer, export feature-extraction code formatted for streaming data that is MATLAB Coder™ compliant and a Simulink^® model that contains that code.
Anatomy of App-Generated MATLAB Code
Explore the functional organization of MATLAB code generated in Diagnostic Feature Designer.
Data Ensembles for Condition Monitoring and Predictive Maintenance
Algorithm design with Predictive Maintenance Toolbox™ uses data organized in ensembles. You can generate ensemble data from a Simulink model or create ensembles from existing data stored on disk.