OPC UA

Access OPC UA data from MATLAB and Simulink

OPC Unified Architecture (OPC UA) is an industrial communication standard developed by the OPC Foundation. OPC is vendor independent and supports all major industrial automation platforms.

OPC UA is a data exchange standard for safe, reliable, manufacturer-independent, and platform-independent industrial communication. It enables secure data exchange between hardware platforms from different vendors and across operating systems. Engineers need to access data from PLCs and industrial PCs in order to analyze the productivity of production lines, optimize machine parameters, or plan service intervals (predictive maintenance). OPC Unified Architecture is a standard protocol for accessing this—live and historical—data.

When accessing data from PLCs or industrial PCs, engineers usually have to deal with vendor-specific industrial fieldbuses or implement Ethernet-based data exchange mechanisms (e.g., over TCP/IP or UDP). Reading data in MATLAB® or Simulink® and writing parameters to industrial devices becomes easy and vendor independent with OPC UA. This approach enables the user to directly benefit from performing data analytics and other capabilities with MATLAB.

Industrial IoT with OPC UA

In the development of industrial Internet of Things (IIoT) applications, OPC UA is often used for the communication between edge nodes and the data aggregator. Machine (M2M) interactions using OPC UA offer wide interoperability for IoT systems. Big data solutions can easily be implemented within the MATLAB workflow.

OPC UA as standardized communication protocol in industrial IoT solutions.

Predictive Maintenance with OPC UA

OPC UA is supported in OPC Toolbox™. With OPC Toolbox engineers can easily acquire machine data for use with statistical, machine learning, and system identification methods from MATLAB in order to create algorithms for predictive maintenance.

The OPC Data Access Explorer app in MATLAB.

See also: data analytics, big data with MATLAB, Internet of Things, predictive maintenance