Deploy Predictive Maintenance Algorithms

Implement and deploy condition-monitoring and predictive maintenance algorithms

Deployment or integration of a predictive maintenance algorithm is typically the final stage of the algorithm-development workflow. How you ultimately deploy the algorithm can also be a consideration in earlier stages of algorithm design. MathWorks^® products support several phases of the process for developing, deploying, and validation process for predictive maintenance algorithms.

Functions

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Generate and Compile Code

`codegen`	Generate C/C++ code from MATLAB code
`mcc`	Compile MATLAB functions for deployment

Deploy RUL Prediction Algorithms

`saveRULModelForCoder`	Save RUL model for use in code generation
`loadRULModelForCoder`	Load and reconstruct RUL model from file for use in code generation
`readState`	Get degradation RUL model state for use at runtime
`restoreState`	Restore degradation RUL model state at runtime

Topics

Deployment Basics

Deploy Predictive Maintenance Algorithms
Understand the phases of deployment and implementation of your predictive-maintenance algorithm.

Deploy RUL Prediction Algorithms

Generate Code for Predicting Remaining Useful Life
Deploy an algorithm for predicting remaining useful life (RUL). Such code generation is useful when you have trained an RUL model and are ready to deploy the prediction algorithm to another environment.
Generate Code That Preserves RUL Model State for System Restart
Generate code that preserves the state of the RUL model when the prediction algorithm is stopped and restarted.