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.