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Import Multimember Ensemble

Import Multimember Ensemble

The app accepts input data in the form of individual member datasets or ensemble datasets. Import multimember ensemble datasets when your source data is combined into one collective dataset that includes data for all members. This collective dataset can be any of the following:

  • An ensemble table containing table arrays or matrices. Table rows represent individual members.

  • An ensemble cell array containing tables or matrices. Cell array rows represent individual members.

  • An ensemble datastore object that contains the information necessary to interact with files stored externally to the app. Use an ensemble datastore object especially when you have too much data to fit into app memory.

The members in the collective dataset must all contain the same independent variables, data variables, and condition variables.

  • All independent time variables must be of the same type — either all double or all duration or all datetime. If your original data was uniformly sampled and timestamps were not recorded, the app prompts you to construct a uniform timeline during the import process

  • Embedded matrices can contain only one independent variable, but can have any number of data variables tied to that independent variable.

  • Condition variables in a member dataset contain a single scalar. The form of the scalar can be numeric, string, cell, or categorical.

If you are using an ensemble datastore object, you must set the ReadSize property to 1. This property determines how many members the software reads in one operation. The app reads one member at a time. For more information on ReadSize, see the property description in fileEnsembleDatastore or simulationEnsembleDatastore.

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

Before importing your data, it must already be clean, with preprocessing such as outlier and missing-value removal. For more information, see Data Preprocessing for Condition Monitoring and Predictive Maintenance.


Select a single dataset from Ensemble variable. You cannot import data incrementally.


Review and modify the variable types and units that Diagnostic Feature Designer associates with your imported variables.

If a table variable consists of a timetable array or a table array with its own variable names, then the imported variable name combines these names. For example, if table variable Vibration is a timetable with Time and Data variables, then the imported variable names are Vibration/Time and Vibration/Data.

The import process infers the variable type from its source and type. Sometimes, the type or the unit is ambiguous, and you must update the default setting.

  • Numeric scalars represent either condition variables or features. By default, when you import tables, the app treats numeric scalars as features. If the default type is incorrect, select the correct variable type.

  • Independent variable type is explicit in timetables, but not in tables or matrices. Select the correct independent variable type for any unidentified independent variables.

  • Update units for variables if necessary by selecting or entering alternatives within Units.

Uniformly sampled data does not always have explicitly recorded timestamps. The app detects when your imported data does not contain an explicit independent variable and allows you to create a uniform one. Specify the type, starting value, and sampling interval.


Review the ensemble variables that result from your import. Each of these variables is an ensemble variable that contains information from all your imported members. The app maintains these variables in Ensemble name. Update the default if you want to use a different ensemble name.

Click Import once you are confident your ensemble is complete. If, after completing the import, you find that you need additional data, you must perform a fresh import that includes everything you want. This fresh import deletes existing imported variables, derived variables, and features.

Consider saving your session immediately after you import if you plan to explore the data in multiple sessions. Saving your session after import provides you with an option for a clean start for new sessions without needing to import your ensemble dataset again. You can save additional sessions after you have generated derived variables and features.

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