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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


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pcaPrincipal component analysis of raw data
pcaresResiduals from principal component analysis
sequentialfsSequential feature selection using custom criterion
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
fitcsvmTrain support vector machine (SVM) classifier for one-class and binary classification
fitcecocFit multiclass models for support vector machines or other classifiers
fitcknnFit k-nearest neighbor classifier
fitclinearFit binary linear classifier to high-dimensional data
fitcnbTrain multiclass naive Bayes model
fitctreeFit binary decision tree for multiclass classification
fitckernelFit binary Gaussian kernel classifier 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-domain or frequency-domain data
arxEstimate parameters of ARX, ARIX, AR, or ARI model
armaxEstimate parameters of ARMAX, ARIMAX, ARMA, or ARIMA model using time-domain data
arEstimate parameters of AR model or ARI 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 to reference samples
segmentSegment data and estimate models for each segment


Decision Models

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 Using Model-Based Approach

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.

Fault Detection Using System Identification

Fault Detection Using Data Based Models

Use a data-based modeling approach for fault detection.

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.

Detect Abrupt System Changes Using Identification Techniques

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

Multiclass Fault Detection

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.

Analyze and Select Features for Pump Diagnostics

Use the Diagnostic Feature Designer app to analyze and select features to diagnose faults in a triplex reciprocating pump.

Fault Detection and Diagnosis Using Artificial Intelligence

Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

Rolling Element Bearing Fault Diagnosis Using Deep Learning

This example shows how to perform fault diagnosis of a rolling element bearing using a deep learning approach.

Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data

Detect anomalies in industrial-machine vibration data using machine learning and deep learning.