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Detect and Diagnose Faults

Train classifiers or regression models for condition monitoring

To design an algorithm for detecting and diagnosing faults, you use condition indicators extracted from system data to train a decision model that can analyze test data to determine the current system state.

When designing your algorithm, you might test different fault detection and diagnosis models using different condition indicators. Thus, this step in the design process is likely iterative with the step of extracting condition indicators, as you try different indicators, different combinations of indicators, and different decision models.

For an overview of the types of models you can use, see Decision Models for Fault Detection and Diagnosis

Functions

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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
ksdensityKernel smoothing function estimate for univariate and bivariate data
histfitHistogram with a distribution fit
coxphfitCox proportional hazards regression
ztestz-test
fitcsvmTrain binary support vector machine (SVM) classifier
fitcecocFit multiclass models for support vector machines or other classifiers
fitcknnFit k-nearest neighbor classifier
fitclinearFit linear classification model to high-dimensional data
fitcnbTrain multiclass naive Bayes model
fitctreeFit binary classification decision tree for multiclass classification
fitckernelFit Gaussian kernel classification model using random feature expansion
kmeansk-means clustering
mleMaximum likelihood estimates
TreeBaggerCreate bag of decision trees
nlarxEstimate parameters of nonlinear ARX model
ssestEstimate state-space model using time or frequency domain data
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
forecastForecast identified model output
translatecovTranslate parameter covariance across model transformation operations
controlchartShewhart control charts
controlrulesWestern Electric and Nelson control rules
cusumDetect small changes in mean using cumulative sum
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
pdistPairwise distance between pairs of observations
pdist2Pairwise distance between two sets of observations
mahalMahalanobis distance
segmentSegment data and estimate models for each segment

Topics

Decision Models for Fault Detection and Diagnosis

Use condition indicators extracted from healthy and faulty data to train classifiers or regression models for detecting and diagnosing faults.

Fault Diagnosis of Centrifugal Pumps Using Steady State Experiments

Use a model-based approach for detection and diagnosis of different types of faults in a pumping system.

Fault Diagnosis of Centrifugal Pumps Using Residual Analysis

Use a model parity-equations-based approach for detection and diagnosis of faults in a pumping system.

Multi-Class Fault Detection Using Simulated Data

Use a Simulink model to generate faulty and healthy data, and use the data to develop a multi-class classifier to detect different combinations of faults.

Fault Detection Using an Extended Kalman Filter

Use an extended Kalman filter for online estimation of the friction of a simple DC motor. Significant changes in the estimated friction are detected and indicate a fault.

Fault Detection Using Data Based Models

Use a data-based modeling approach for fault detection.

Detect Abrupt System Changes Using Identification Techniques

Detect abrupt changes in the behavior of a system using online estimation and automatic data segmentation techniques.

Related Information

Classification Learner App (Statistics and Machine Learning Toolbox)

Regression Learner App (Statistics and Machine Learning Toolbox)