Get Started with Predictive Maintenance Toolbox
Predictive Maintenance Toolbox™ lets you label 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 such as bearings and gearboxes 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 analyze and label sensor data imported from local files, cloud storage, and distributed file systems. You can also 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.
Tutorials
Identify Condition Indicators for Predictive Maintenance Algorithm Design
This three-part tutorial shows you how to work with ensemble data and extract and rank features in Diagnostic Feature Designer.
About Condition Monitoring and Predictive Maintenance
- Designing Algorithms for Condition Monitoring and Predictive Maintenance
Predictive Maintenance Toolbox helps you identify condition indicators in our data and design algorithms for monitoring system condition and predicting remaining useful life.
Videos
Predictive Maintenance Part 1: Introduction
Learn about different maintenance strategies and predictive
maintenance workflow. Predictive maintenance lets you find the
optimum time to schedule maintenance by estimating time to
failure.
Predictive Maintenance Part 2: Feature Extraction for Identifying
Condition Indicators
Learn how to extract condition indicators from your data.
Condition indicators help you distinguish between healthy and faulty
states of a machine.
Predictive Maintenance Part 3: Remaining Useful Life
Estimation
Predictive maintenance lets you estimate the remaining useful life
(RUL) of your machine. Explore three common models to estimate RUL:
similarity, survival, and degradation
Predictive Maintenance Part 4: How to Use Diagnostic Feature Designer
for Feature Extraction
Learn how you can extract time-domain and spectral features using
Diagnostic Feature Designer for developing your predictive
maintenance algorithm.