Predictive Maintenance Toolbox


Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

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.

Fault and Anomaly Detection

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

RUL Estimation

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

Application-Specific Algorithms

Use component-specific functions to develop algorithms to detect battery anomalies, classify bearing faults, detect leaks in pumps, track changes in motor performance, and more. Get started quickly with a library of reference examples.

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.

Failure Data Generation

Generate simulated failure and degradation data using Simulink and Simscape™ models of your machine. Modify parameter values, inject faults, and change model dynamics. Create digital twins to monitor performance and predict future behavior.

Code Generation

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

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.

Predictive Maintenance Video Series

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

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