Predictive Maintenance Toolbox


Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

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Fault Detection and Remaining Useful Life (RUL) Estimation

Detect anomalies, diagnose the root cause of faults, and estimate RUL using machine learning and time-series models.

Fault and Anomaly Detection

Track changes in your system to determine the presence of anomalies and faults using changepoint detection, Kalman filters, and control charts.

Anomaly detection using spectral peaks extracted from data-based state-space models.

Fault detection using data-based models.

RUL Estimation Models

Estimate the RUL of a machine to help you predict its time to failure and optimize maintenance schedules. The type of RUL estimation algorithm used depends on the condition indicators extracted from the data, as well as how much data is available.

Similarity, degradation, and survival RUL models.

Similarity, degradation, and survival RUL models.

Fault Diagnosis Using Classification Models

Isolate the root cause of a failure by training classification and clustering models using support vector machines, k-means clustering, and other machine learning techniques.

Scatter plot shows correct and incorrect predictions made by the trained classification model.

Diagnosing faults using Classification Learner app.

Condition Indicator Design

Extract features from sensor data using signal-based and model-based approaches. Use extracted features as inputs to diagnostic and machine learning algorithms.

Diagnostic Feature Designer App

Extract, visualize, and rank features to design condition indicators for monitoring machine health. Generate MATLAB code from the app to automate the entire process.

Signal-Based Condition Indicators

Extract features from raw or preprocessed sensor data using rainflow counting, spectral peak detection, spectral kurtosis, and other time, frequency, and time-frequency domain techniques. Use Live Editor Tasks to interactively perform phase space reconstruction and extract nonlinear signal features.

Spectral kurtosis varying over time and frequency for vibration data measured from a bearing.

Time-frequency-based condition indicator.

Model-Based Condition Indicators

Fit linear and nonlinear time-series models, state-space models, and transfer function models to sensor data. Use the properties and characteristics of these fitted models as condition indicators.

10th-order autoregressive models fit to vibration data from damaged and undamaged systems.

Autoregressive model-based condition indicator.

Deployment to Edge and Cloud

Deploy condition monitoring and predictive maintenance algorithms to edge devices or to production applications in the cloud

Edge Deployment

Use MATLAB Coder™ to generate C/C++ code for RUL models and feature computations.

Deploying predictive maintenance algorithms to a PLC

Deploying predictive maintenance algorithms to a PLC.

Cloud Deployment

Use MATLAB Compiler™ and MATLAB Compiler SDK™ to deploy predictive maintenance algorithms as C/C++ shared libraries, web apps, Docker containers, Microsoft® .NET assemblies, Java® classes, and Python® packages. Deploy generated libraries to MATLAB Production Server™ on Microsoft® Azure®, AWS®, or dedicated on-prem servers without recoding or creating custom infrastructure.

Components of a deployed predictive maintenance system

Components of a deployed predictive maintenance system.

Reference Examples for Algorithm Development

Develop condition monitoring and predictive maintenance algorithms for batteries, gearboxes, pumps, and other machines.

Bearings and Gearboxes

Develop algorithms for classifying inner and outer race faults, detecting gear tooth faults, and estimating RUL.

Alpha-lambda plot for RUL estimation of a wind turbine bearing.

RUL estimation for a wind turbine bearing.

Pumps, Motors, and Batteries

Develop algorithms for detecting leaks and clogs in pumps, tracking changes in motor friction, and estimating battery degradation over time.

Simulink and Simscape model of a triplex reciprocating pump.

Fault classification in a triplex pump.

Data Management

Access data wherever it lives. Generate simulation data from Simulink models to represent machine failures in the absence of real sensor data.

Data Import and Organization

Import data from local files, Amazon S3™, Windows Azure® Blob Storage, Hadoop® Distributed File System, OPC UA servers, and the OSIsoft PI System

Data ensemble for managing vibration data from a ball bearing.

Managing multiple files by using a data ensemble.

Failure Data Generation from Simulink and Simscape

Simulate and label failure data using Simulink and Simscape™ models of your machine. Modify parameter values, inject faults, and change model dynamics.

Simulation data ensemble for managing failure data generated from a Simulink model of a transmission casing.

Managing data using simulation data ensembles.

Predictive Maintenance Video Series

Watch the videos in this series to learn about predictive maintenance.