Predictive Maintenance Toolbox
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.
Learn the basics of Predictive Maintenance Toolbox
Apply predictive maintenance methods to component-specific workflows such as for rotating machinery or battery systems
Import measured data, generate simulated data, organize data for use at the command line and in the app
Clean and label data in preparation for more advanced signal processing
Explore data at the command line or interactively to identify features that can indicate system state or predict future states
Train statistical, machine learning, and deep learning models for condition monitoring and anomaly detection
Predict RUL using specialized models designed for computing RUL from system data, state estimators, or identified models
Implement and deploy condition-monitoring and predictive maintenance algorithms
Apply deep learning and machine learning techniques to predictive maintenance workflows