MathWorks Predictive Maintenance Toolbox

MathWorks has announced Predictive Maintenance Toolbox, designed to help engineers design and test condition monitoring and predictive maintenance algorithms. The product offers capabilities and reference examples for engineers who are designing algorithms to organise data, design condition indicators, monitor machine health and estimate remaining useful life (RUL) to prevent equipment failures.

With Predictive Maintenance Toolbox, engineers can analyse and label sensor data imported from files that are stored locally or on cloud storage. They can also label simulated failure data generated from Simulink models to represent equipment failures.

Signal processing and dynamic modelling methods that build on techniques such as spectral analysis and time series analysis let engineers pre-process data and extract features that can be used to monitor the condition of the machine. Using survival, similarity and trend-based models to predict the RUL helps engineers estimate a machine’s time to failure. The toolbox includes reference examples for motors, gearboxes, batteries and other machines that can be re-used for developing custom predictive maintenance and condition monitoring algorithms.

The product allows engineers to develop and validate the algorithms needed to predict when an equipment failure might occur or to detect any underlying anomalies by monitoring sensor data.