Identify Condition Indicators

Explore data at the command line or in the app to identify features that can indicate system state or predict future states

A condition indicator is a feature of system data whose behavior changes in a predictable way as the system degrades or operates in different operational modes. A condition indicator can be any feature that is useful for distinguishing normal from faulty operation or for predicting remaining useful life. A useful condition indicator clusters similar system status together, and sets different status apart.

You can derive condition indicators from signal analysis, by extracting time-domain or frequency-domain features of system data. You can also derive condition indicators by fitting static or dynamic models to your data, and examining model parameters or model behavior to distinguish fault states or predict system degradation. For more information, see Condition Indicators for Monitoring, Fault Detection, and Prediction.

The Diagnostic Feature Designer app lets you extract features from your data interactively. Within the app, you can prepare your data for feature extraction, extract features and visualize their effectiveness, and rank features using various statistical algorithms. For more information on the app, see Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer.

Apps

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

Functions

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Time-Domain Features

meanAverage or mean value of array
movmeanMoving mean
medianMedian value of array
stdStandard deviation of timeseries data
rmsRoot-mean-square level
movmadMoving median absolute deviation
peak2peakMaximum-to-minimum difference
skewnessSkewness
kurtosisKurtosis
envelopeSignal envelope
dtwDistance between signals using dynamic time warping
rainflowRainflow counts for fatigue analysis
approximateEntropyMeasure of regularity of nonlinear time series
correlationDimensionMeasure of chaotic signal complexity
lyapunovExponentCharacterize the rate of separation of infinitesimally close trajectories
phaseSpaceReconstructionConvert observed time series to state vectors

Rotating Machinery Features

gearConditionMetricsStandard metrics for gear condition monitoring

Frequency-Domain Features

powerbwPower bandwidth
modalfrfFrequency-response functions for modal analysis
bandpowerBand power
meanfreqMean frequency
medfreqMedian frequency
sfdrSpurious free dynamic range
sinadSignal to noise and distortion ratio
snrSignal-to-noise ratio
thdTotal harmonic distortion
obwOccupied bandwidth
findpeaksFind local maxima

Time-Frequency Features

pentropySpectral entropy of signal
pkurtosisSpectral kurtosis from signal or spectrogram
spectrogramSpectrogram using short-time Fourier transform
tfmomentJoint moment of the time-frequency distribution of a signal
tfsmomentConditional spectral moment of the time-frequency distribution of a signal
tftmomentConditional temporal moment of the time-frequency distribution of a signal
instfreqEstimate instantaneous frequency

Model Fitting

ssestEstimate state-space model using time or frequency domain data
nlarxEstimate parameters of nonlinear ARX model
arxEstimate parameters of ARX or AR model using least squares
armaxEstimate parameters of ARMAX model using time-domain data
arEstimate parameters of AR model for scalar time series
pemPrediction error estimate for linear and nonlinear model
modalfitModal parameters from frequency-response functions
modalfrfFrequency-response functions for modal analysis
segmentSegment data and estimate models for each segment

Recursive Model Fitting

recursiveARCreate System object for online parameter estimation of AR model
recursiveARMACreate System object for online parameter estimation of ARMA model
recursiveARMAXCreate System object for online parameter estimation of ARMAX model
recursiveBJCreate System object for online parameter estimation of Box-Jenkins polynomial model
recursiveLSCreate System object for online parameter estimation using recursive least squares algorithm
recursiveOECreate System object for online parameter estimation of Output-Error polynomial model
recursiveARXCreate System object for online parameter estimation of ARX model

Recursive State Estimation

unscentedKalmanFilterCreate unscented Kalman filter object for online state estimation
extendedKalmanFilterCreate extended Kalman filter object for online state estimation
particleFilterParticle filter object for online state estimation

Model Dynamics

dampNatural frequency and damping ratio
polePoles of dynamic system
zeroZeros and gain of SISO dynamic system

Simulation

simSimulate response of identified model
residCompute and test residuals
pcaPrincipal component analysis of raw data
pcaresResiduals from principal component analysis
sequentialfsSequential feature selection
fscncaFeature selection using neighborhood component analysis for classification
tsnet-Distributed Stochastic Neighbor Embedding

Topics

Condition Indicators Basics

Condition Indicators for Monitoring, Fault Detection, and Prediction

A condition indicator is any feature of system data whose behavior changes in a predictable way as the system degrades.

Signal-Based Condition Indicators

A signal-based condition indicator is a quantity derived from processing of signal data. The condition indicator captures some feature of the signal that changes as system performance degrades.

Model-Based Condition Indicators

A model-based condition indicator is a quantity derived from fitting system data to a model and performing further processing using the model. The condition indicator captures some feature of the model that changes as system performance degrades.

Condition Indicators in the Diagnostic Feature Designer

Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer

Workflow for interactively exploring and processing ensemble data, designing and ranking features from that data, and exporting data and selected features.

Process Data and Explore Features in Diagnostic Feature Designer

Filter and transform data within the app. Extract features from the imported and derived signals, and assess feature effectiveness.

Interpret Feature Histograms in Diagnostic Feature Designer

Interpret feature histograms to assess how well each feature separates labeled groups of data

Condition Indicators for Rotating Machinery

Condition Indicators for Gear Condition Monitoring

Workflow to identify condition indicators for gear condition monitoring, and their evaluation.

Featured Examples