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Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

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 rotating machines by extracting features from vibration data using frequency and time-frequency methods. 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, 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.

Get Started

Learn the basics of Predictive Maintenance Toolbox

Manage System Data

Import measured data, generate simulated data, organize data for use at the command line and in the app

Preprocess Data

Clean and transform data to prepare it for extracting condition indicators at the command line and in the app

Identify Condition Indicators

Explore data at the command line or in the app to identify features that can indicate system state or predict future states

Detect and Predict Faults

Train decision models for condition monitoring and fault detection; predict remaining useful life (RUL)

Deploy Predictive Maintenance Algorithms

Implement and deploy condition-monitoring and predictive-maintenance algorithms