In algorithm design for predictive maintenance, Data preprocessing is often necessary to clean the data and convert it into a form from which you can extract condition indicators. You can perform data preprocessing on arrays or tables of measured or simulated data that you manage with Predictive Maintenance Toolbox™ ensemble datastores. For an overview of some common types of data preprocessing, see Data Preprocessing for Condition Monitoring and Predictive Maintenance.
The Diagnostic Feature Designer app lets you perform many preprocessing operations interactively. The processing tools in the app include filtering, time-domain processing, frequency-domain processing, and interpolation. App time-domain processing options include specialized filtering for rotating machinery. For more information on the app, see Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer.
|Diagnostic Feature Designer||Interactively extract, visualize, and rank features from measured or simulated data for machine diagnostics and prognostics|
|Time-synchronous signal average|
|Difference signal of a time-synchronous averaged signal|
|Regular signal of a time-synchronous averaged signal|
|Residual signal of a time-synchronous averaged signal|
|Track and extract order magnitudes from vibration signal|
|Track and extract RPM profile from vibration signal|
|Analyze signals in the frequency and time-frequency domains|
|Envelope spectrum for machinery diagnosis|
|Average spectrum versus order for vibration signal|
|Frequency-response functions for modal analysis|
|Generate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction|
|Construct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction|
|Generate fault frequency bands for spectral feature extraction|
Use signal-processing techniques to preprocess data, cleaning it and converting it into a form from which you can extract condition indicators. Knowledge of your system can help you choose an appropriate preprocessing approach.
Follow this workflow for interactively exploring and processing ensemble data, designing and ranking features from that data, and exporting data and selected features, and generating MATLAB code.
Organize measurements and information for multiple systems into data sets that you can import into the app.
Filter and transform data within the app. Extract features from the imported and derived signals, and assess feature effectiveness.