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.
Learn the basics of Predictive Maintenance Toolbox
Import measured data, generate simulated data
Clean and transform data to prepare it for extracting condition indicators
Explore data to identify features that can indicate system state or predict future states
Train decision models for condition monitoring and fault detection; predict remaining useful life (RUL)
Implement and deploy condition-monitoring and predictive-maintenance algorithms