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.
|Diagnostic Feature Designer||Interactively extract, visualize, and rank features from measured or simulated data for machine diagnostics and prognostics|
|Average or mean value of array|
|Median value of array|
|Standard deviation of timeseries data|
|Moving median absolute deviation|
|Distance between signals using dynamic time warping|
|Rainflow counts for fatigue analysis|
|Measure of regularity of nonlinear time series|
|Measure of chaotic signal complexity|
|Characterize the rate of separation of infinitesimally close trajectories|
|Convert observed time series to state vectors|
|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|
|Spectral metrics for the specified fault frequency bands of the power spectral density (PSD)|
|Standard metrics for gear condition monitoring|
|Frequency-response functions for modal analysis|
|Spurious free dynamic range|
|Signal to noise and distortion ratio|
|Total harmonic distortion|
|Find local maxima|
|Spectral entropy of signal|
|Spectral kurtosis from signal or spectrogram|
|Spectrogram using short-time Fourier transform|
|Joint moment of the time-frequency distribution of a signal|
|Conditional spectral moment of the time-frequency distribution of a signal|
|Conditional temporal moment of the time-frequency distribution of a signal|
|Estimate instantaneous frequency|
|Estimate state-space model using time-domain or frequency-domain data|
|Estimate parameters of nonlinear ARX model|
|Estimate parameters of ARX, ARIX, AR, or ARI model|
|Estimate parameters of ARMAX model using time-domain data|
|Estimate parameters of AR model or ARI model for scalar time series|
|Prediction error estimate for linear and nonlinear model|
|Modal parameters from frequency-response functions|
|Frequency-response functions for modal analysis|
|Segment data and estimate models for each segment|
|Create System object for online parameter estimation of AR model|
|Create System object for online parameter estimation of ARMA model|
|Create System object for online parameter estimation of ARMAX model|
|Create System object for online parameter estimation of Box-Jenkins polynomial model|
|Create System object for online parameter estimation using recursive least squares algorithm|
|Create System object for online parameter estimation of Output-Error polynomial model|
|Create System object for online parameter estimation of ARX model|
|Create unscented Kalman filter object for online state estimation|
|Create extended Kalman filter object for online state estimation|
|Particle filter object for online state estimation|
|Natural frequency and damping ratio|
|Poles of dynamic system|
|Zeros and gain of SISO dynamic system|
|Reconstruct Phase Space||Reconstruct phase space of a uniformly sampled signal in the Live Editor|
|Estimate Approximate Entropy||Interactively estimate the approximate entropy of a uniformly sampled signal in the Live Editor|
|Estimate Correlation Dimension||Estimate the correlation dimension of a uniformly sampled signal in the Live Editor|
|Estimate Lyapunov Exponent||Interactively estimate the Lyapunov exponent of a uniformly sampled signal in the Live Editor|
A condition indicator is any feature of system data whose behavior changes in a predictable way as the system degrades.
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.
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.
Workflow for interactively exploring and processing ensemble data, designing and ranking features from that data, and exporting data and selected features.
Filter and transform data within the app. Extract features from the imported and derived signals, and assess feature effectiveness.
Interpret feature histograms to assess how well each feature separates labeled groups of data.
This example shows how to isolate a shaft fault from simulated measurement data for machines with varying rotation rates.
Use Live Editor tasks to reconstruct phase space of a uniformly sampled signal and then use the reconstructed phase space to estimate the correlation dimension and the Lyapunov exponent.
Workflow to identify condition indicators for gear condition monitoring, and their evaluation.
This example illustrates how current signature analysis can be applied to extract spectral metrics to detect faults in specific drive gears of a hobby-grade electric servo.