The app accepts input data in the form of individual single-member datasets or an ensemble dataset. Import single-member datasets when:
Your source data in the MATLAB® workspace consists of an individual workspace variable for each machine member.
The size and number of your member datasets are small enough for app memory to accommodate
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.
|Data Variables||Can contain timetables, tables, cell arrays, or numeric arrays|
|Independent Time Variables||Double, ||All time variables must be of the same type. If your data was uniformly sampled and you don't have recorded timestamps, you can construct a uniform timeline during the import process.|
|Condition Variables||Scalar — Numeric, string, cell, or categorical||You can import condition variables along with your data in tables, timetables, or cell arrays, but not in matrices.|
|Member Matrices||Purely numeric array. Only one independent variable, but any number of data variables tied to that independent variable||Cannot accommodate variable names|
For more information about organizing your data for import, see Organize System Data for Diagnostic Feature Designer.
Select the same-size datasets you want to import from your workspace. Import all the datasets you want to use in your session at one time. You cannot import data incrementally.
Review and modify the variable types and units that Diagnostic Feature Designer associates with your imported variables.
In Variable Name, the configuration view displays the name of the
variable as it will appear after the import. If a table variable consists of a timetable or
table with its own variable names, then the app combines these variable names into a new
name. For example, if table variable
Vibration is a
variables, then the imported variable names are
In Variable Type, the app infers the variable type from its source. Sometimes, the variable type or unit is ambiguous, and you must update the default setting.
Numeric scalars can represent either condition variables or features. By default,
the app assumes numeric scalars are of type
feature. If your
scalar is actually a condition variable, change the variable type to
Independent variable assignment is explicit in timetables, but not in tables or matrices. If the configuration table shows the wrong variable type for an independent variable, select the correct variable type.
In a matrix, you can use only one independent variable. If you have multiple identical independent variables, such as a timeline that applies to all the data, select Skipfor the redundant variables.
In Unit, the configuration view displays the units associated with the variables. If the unit specification for a variable is incorrect, update Unit by selecting or entering an alternative.
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 signal, spectrum, or feature that contains information from all your imported members. The app maintains these variables in an ensemble with the name specified in Ensemble name. Update the default name 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 datasets, you must perform a fresh import that includes everything you want. This fresh import deletes existing imported variables, derived variables, and features.
if you plan to explore the data in multiple sessions, consider saving your session immediately after you import. Saving your session after import provides you with an option for a clean start for new sessions without needing to import your separate files again. You can save additional sessions after you have generated derived variables and features.