Detect and Diagnose Faults

Train statistical, machine learning, and deep learning models for condition monitoring and anomaly detection. Use distance and matrix profiling to detect anomalies from machine data

Condition monitoring includes discriminating between faulty and healthy states (fault detection) or, when a fault state is present, determining the source of the fault (fault diagnosis). To design an algorithm for condition monitoring, 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.

An alternate approach to condition monitoring is to evaluate the raw data or derived condition indicators for patterns that are known to indicate healthy behavior. Deviations from these patterns, as expressed by distance measurements, can indicate anomalous behavior.

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

Functions

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Distance and Matrix Profiling

`similarityDistance`	Compute distance profile between query and time series subsequences by evaluating z-normalized Euclidean distances (Since R2024b)
`distanceProfile`	Compute distance profile between query subsequence and all other subsequences of a time series (Since R2024b)
`matrixProfile`	Compute matrix profile of time series (Since R2024b)
`findDiscord`	Find the locations of top discords from the matrix profile of a time series (Since R2024b)

Feature Selection

`pca`	Principal component analysis of raw data
`pcares`	Residuals from principal component analysis
`sequentialfs`	Sequential feature selection using custom criterion
`fscnca`	Feature selection using neighborhood component analysis for classification
`tsne`	t-Distributed Stochastic Neighbor Embedding

Statistical Distribution Fitting

`ksdensity`	Kernel smoothing function estimate for univariate and bivariate data
`histfit`	Histogram with a distribution fit
`coxphfit`	Cox proportional hazards regression
`ztest`	z-test

Machine Learning

`fitcsvm`	Train support vector machine (SVM) classifier for one-class and binary classification
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`fitcknn`	Fit k-nearest neighbor classifier
`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitcnb`	Train multiclass naive Bayes model
`fitctree`	Fit binary decision tree for multiclass classification
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`kmeans`	k-means clustering
`mle`	Maximum likelihood estimates
`TreeBagger`	Ensemble of bagged decision trees

Regression with Dynamic Models

`nlarx`	Estimate parameters of nonlinear ARX model
`ssest`	Estimate state-space model using time-domain or frequency-domain data
`arx`	Estimate parameters of ARX, ARIX, AR, or ARI model
`armax`	Estimate parameters of ARMAX, ARIMAX, ARMA, or ARIMA model using time-domain data
`ar`	Estimate parameters when identifying AR model or ARI model for scalar time series
`forecast`	Forecast time-series values into future
`translatecov`	Translate parameter covariance across model transformation operations

Control Charts

`controlchart`	Shewhart control charts
`controlrules`	Western Electric and Nelson control rules
`cusum`	Detect small changes in mean using cumulative sum

Changepoint Detection

`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`pdist`	Pairwise distance between pairs of observations
`pdist2`	Pairwise distance between two sets of observations
`mahal`	Mahalanobis distance to reference samples
`segment`	Segment data and estimate models for each segment

Battery Management

`meanDifferenceModel`	Identify most degraded cell in serially connected lithium-ion battery pack (Since R2022b)
`adjacentPairCorrelationModel`	Identify worst cell relative to other cells in serially connected lithium-ion battery pack (Since R2023a)

Topics

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.
Power Converter Aging Severity Detection
Generate synthetic semiconductor degradation data from a power converter model, and use that data to build a predictive maintenance algorithm that can detect aging severity in a power converter.

Related Information

Featured Examples

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.

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Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data

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

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Broken Rotor Fault Detection in AC Induction Motors Using Vibration and Electrical Signals

Use the Diagnostic Feature Designer app to detect broken rotor faults in AC induction motors using vibration and electrical signals.

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