Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

Predictive Maintenance Toolbox provides functions and apps for designing condition monitoring and predictive maintenance algorithms for motors, gearboxes, bearings, batteries, and other applications. The toolbox lets you design condition indicators, detect faults and anomalies, and estimate remaining useful life (RUL).

With the Diagnostic Feature Designer app, you can interactively extract time, frequency, time-frequency, and physics-based features. You can rank and export the features to develop application-specific algorithms for fault and anomaly detection. To estimate RUL, you can use survival, similarity, and trend-based models.

The toolbox helps you organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. You can generate simulated failure data from Simulink and Simscape models.

To operationalize your algorithms, you can generate C/C++ code for edge deployment or create production applications for cloud deployment. The toolbox includes application-specific reference examples that you can reuse for developing and deploying custom predictive maintenance algorithms.

What Is Predictive Maintenance Toolbox?

Discover the latest features in the release notes

The Diagnostic Feature Designer app shows signal data in four panes: signal traces, power spectra, a table of features ranked by one-way ANOVA, and a bar chart sorting the features by importance.

Feature Engineering

Use the Diagnostic Feature Designer app or programmatically extract and rank features from sensor data with signal-based and model-based approaches for fault detection and prediction with AI.

Feature Design for Predictive Maintenance (3:03)

Documentation | Examples

A plot of two groups of power spectra. The black group is labeled normal and the red group is labeled faulty. The red group has larger magnitude peaks at some frequencies.

Fault and Anomaly Detection

Use AI, statistical, and dynamic modeling methods for condition monitoring. Track changes in your system, detect anomalies, and identify faults.

Documentation | Examples

RUL Estimation

Train RUL estimator models on historical data to predict time-to-failure and optimize maintenance schedules.

Three Ways to Estimate Remaining Useful Life

Documentation | Examples

A MATLAB plot of electrical data from a motor with colored bands highlighting the first six harmonic fault bands and side bands.

Rotating Machinery

Extract physics-based features specific to rotating machinery. Classify bearing faults, detect leaks in pumps, track changes in motor performance, identify faults in gearboxes, and more. Get started quickly with a library of reference examples.

Documentation | Examples

MATLAB code that shows how to create a fileEnsembleDatastore from a set of vibration data files stored locally. The output shows the ensemble represented as a tall table.

Data Management and Preprocessing

Access sensor data stored locally or remotely. Prepare data for algorithm development by removing outliers, filtering, and applying various time, frequency, and time-frequency preprocessing techniques.

Documentation | Examples

A Simscape model showing a pump housing, three plungers, and a crankshaft connected together.

Failure Data Generation

Simulate rare faults and degradations using physics-based models built in Simulink and Simscape. Modify parameter values, inject faults, and change model dynamics. Create digital twins to monitor performance and predict future behavior.

What is a Digital Twin? (8:28)

Documentation | Examples

A MATLAB Coder Report shows MATLAB code for a Remaining Useful Life prediction function on the left and corresponding C++ code on the right. A colorful region maps a single line of MATLAB code to many lines of C++ code.

Edge Deployment

Use MATLAB Coder to generate C/C++ code directly from feature computation functions, condition monitoring algorithms, and predictive algorithms for real-time edge processing.

Documentation | Examples

Deploy predictive algorithms within your enterprise ecosystem using MATLAB Production Server.

Cloud Deployment

Use MATLAB Compiler and MATLAB Compiler SDK to scale algorithms in the cloud as shared libraries, packages, web apps, Docker containers, and more. Deploy to MATLAB Production Server on Microsoft^® Azure^® or AWS^® without recoding.

Documentation | Examples