Predictive Maintenance Toolbox™ lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine.
The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis. You can monitor the health of batteries, motors, gearboxes, and other machines by extracting features from sensor data. To estimate a machine's time to failure, you can use survival, similarity, and trend-based models to predict the RUL.
You can organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. You can label simulated failure data generated from Simulink® models. The toolbox includes reference examples for motors, gearboxes, batteries, pumps, bearings, and other machines that can be reused for developing custom predictive maintenance and condition monitoring algorithms.
To operationalize your algorithms, you can generate C/C++ code for deployment to the edge or create a production application for deployment to the cloud.
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.
Train RUL estimator models on historical data to predict time-to-failure and optimize maintenance schedules.
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.
Use MATLAB Coder™ to generate C/C++ code directly from feature computation functions, condition monitoring algorithms, and predictive algorithms for real-time edge processing.
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.
Are You a Student?
Your school may already provide access to MATLAB, Simulink, and add-on products through a campus-wide license.