Design and test condition monitoring and predictive maintenance algorithms
Predictive Maintenance Toolbox™ provides tools for labeling data, designing condition indicators, and estimating the remaining useful life (RUL) of a machine. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. You can also label simulated failure data generated from Simulink® models.
Signal processing and dynamic modeling methods that build on techniques such as spectral analysis and time series analysis let you preprocess data and extract features that can be used to monitor the condition of the machine. To estimate a machine's time to failure, you can use survival, similarity, and trend-based models to predict the RUL.
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.
Survival, similarity, and time-series models for remaining useful life (RUL) estimation
Time, frequency, and time-frequency domain feature extraction methods for designing condition indicators
Organizing sensor data imported from local files, Amazon S3™, Windows Azure® Blob Storage, and Hadoop® Distributed File System
Organizing simulated machine data from Simulink models
Examples for developing predictive maintenance algorithms for motors, gearboxes, batteries, and other machines
Model and simulate vehicle dynamics in a virtual 3D environment
Vehicle Dynamics Blockset™ provides fully assembled reference application models that simulate driving maneuvers in a 3D environment. You can use the prebuilt scenes to visualize roads, traffic signs, trees, buildings, and other objects around the vehicle. You can customize the reference models by using your own data or by replacing a subsystem with your own model. The blockset includes a library of components for modeling propulsion, steering, suspension, vehicle bodies, brakes, and tires.
Vehicle Dynamics Blockset™ provides a standard model architecture that can be used throughout the development process. It supports ride and handling analyses, chassis controls development, software integration testing, and hardware-in-the-loop testing. By integrating vehicle dynamics models with a 3D environment, you can test ADAS and automated driving perception, planning, and control software. These models let you test your vehicle with standard driving maneuvers such as a double lane change or with your own custom scenarios.
Preassembled vehicle dynamics models for passenger cars and trucks
Preassembled maneuvers for common ride and handling tests, including a double-lane change
3D environment for visualizing simulations and communicating scene information to Simulink
Libraries of propulsion, steering, suspension, vehicle body, brake, and tire components
Combined longitudinal and lateral slip dynamic tire models
Predictive driver model for generating steering commands that track a predefined path
Prebuilt 3D scenes, including straight roads, curved roads, and parking lots