Getting Started with Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

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

About Condition Monitoring and Predictive Maintenance

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.