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Preprocess Data

Clean and transform data to prepare it for extracting condition indicators at the command line and in the app

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


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fillmissingFill missing values
filloutliersDetect and replace outliers in data
smoothdataSmooth noisy data
movmeanMoving mean
detrendRemove polynomial trend
rescaleScale range of array elements
filter1-D digital filter
designfiltDesign digital filters
tsaTime-synchronous signal average
tsadifferenceDifference signal of a time-synchronous averaged signal
tsaregularRegular signal of a time-synchronous averaged signal
tsaresidualResidual signal of a time-synchronous averaged signal
ordertrackTrack and extract order magnitudes from vibration signal
rpmtrackTrack and extract RPM profile from vibration signal
pspectrumAnalyze signals in the frequency and time-frequency domains
envspectrumEnvelope spectrum for machinery diagnosis
orderspectrumAverage spectrum versus order for vibration signal
modalfrfFrequency-response functions for modal analysis
bearingFaultBandsGenerate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction
gearMeshFaultBandsConstruct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction
faultBandsGenerate fault frequency bands for spectral feature extraction
pentropySpectral entropy of signal
pkurtosisSpectral kurtosis from signal or spectrogram
kurtogramVisualize spectral kurtosis
spectrogramSpectrogram using short-time Fourier transform
hhtHilbert-Huang transform
emdEmpirical mode decomposition