Industrial Communication Toolbox

 

Industrial Communication Toolbox

Exchange data over OPC UA, Modbus, MQTT, and other industrial protocols

Predictive Maintenance and Monitoring

Access plant and manufacturing data to build condition monitoring apps. Together with Predictive Maintenance Toolbox, develop predictive maintenance applications for your industrial process or plant.

OPC UA

Access live and historical plant data securely from OPC UA-compliant servers. Find available nodes on an OPC UA server and read historical data into MATLAB by specifying the nodes and a time range. Use OPC UA subscriptions to monitor changed values and process the data based on conditions you set. Use OPC UA methods to trigger actions on your server.

Modbus

Use apps, MATLAB functions, and Modbus Simulink blocks to send and receive data from industrial controllers such as PLCs over Modbus protocol.

PI Servers

Connect to AVEVA PI System and access time series data from MATLAB for further analysis and visualization and write calculated values back to the PI server.

OPC in Simulink

Connect your Simulink model to simulated or live data using the OPC UA standard. Validate Simulink models for advanced process control or smart manufacturing applications and verify generated code by co-simulation with OPC UA servers running on PLC software such as Codesys.

MQTT

Connect to MQTT compliant brokers over TCP or using WebSocket protocols. Subscribe to topics on the broker and process messages on subscribed topics. Publish messages to topics on the broker.

OPC HDA

Read processed or raw data from OPC Historical Data Access servers, and use MATLAB to interpret, manipulate, and visualize your data.

OPC DA

Exchange data with classic OPC Data Access servers in MATLAB and Simulink.

Deployment Solutions

Use MATLAB Production Server interfaces for OPC UA and for AVEVA PI to deploy your anomaly detection or PdM algorithm in a production environment.

“By partnering with MathWorks Consulting, we developed a robust platform for supervisory control with MATLAB and transitioned our pilot plant to a modern automation control system. This enabled our researchers to rapidly take algorithms from idea to implementation, simulation, and deployment.”

Dr. Ryan Hamilton, Genentech

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