Predictive Maintenance Toolbox provides functions and apps for designing condition monitoring and predictive maintenance algorithms for motors, gearboxes, bearings, batteries, and other applications. The toolbox lets you design condition indicators, detect faults and anomalies, and estimate remaining useful life (RUL).
With the Diagnostic Feature Designer app, you can interactively extract time, frequency, time-frequency, and physics-based features. You can rank and export the features to develop application-specific algorithms for fault and anomaly detection. To estimate RUL, you can use survival, similarity, and trend-based models.
The toolbox helps you organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. You can generate simulated failure data from Simulink and Simscape models.
To operationalize your algorithms, you can generate C/C++ code for edge deployment or create production applications for cloud deployment. The toolbox includes application-specific reference examples that you can reuse for developing and deploying custom predictive maintenance algorithms.