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Predictive Maintenance Toolbox

Design and test condition monitoring and predictive maintenance algorithms

Predictive Maintenance Toolbox™ provides tools for labeling data, designing condition indicators, and estimating the remaining useful life (RUL) of a machine. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. You can also label simulated failure data generated from Simulink® models.

Signal processing and dynamic modeling methods that build on techniques such as spectral analysis and time series analysis let you preprocess data and extract features that can be used to monitor the condition of the machine. To estimate a machine's time to failure, you can use survival, similarity, and trend-based models to predict the RUL.

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.

Getting Started

Learn the basics of Predictive Maintenance Toolbox

Manage System Data

Import measured data, generate simulated data

Preprocess Data

Clean and transform data to prepare it for extracting condition indicators

Identify Condition Indicators

Explore data 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