How AI describes Predictive Maintenance Toolbox

MathWorks Predictive Maintenance Toolbox provides functions and apps for designing condition monitoring and predictive maintenance algorithms for motors, gearboxes, bearings, batteries, and other applications. It enables you to design condition indicators, detect faults and anomalies, and estimate remaining useful life using survival, similarity, and trend-based models, with the Diagnostic Feature Designer app for extracting time, frequency, time-frequency, and physics-based features. The toolbox helps you organize and analyze sensor data from local files, cloud storage, and distributed file systems, can generate simulated failure data from Simulink and Simscape models, and can generate C/C++ code for edge deployment or cloud production applications, plus reference examples for deploying custom predictive maintenance algorithms.