Keeping the Big Data in Control with the Execution of Analytics

Justin Smith, CIO Review Europe | Monday, May 09, 2022

Applying analytics tools in the same engineering platform as the controller, motion control, and HMI can lead to more successful Internet of Things implementations and boost competitive advantages.

FREMONT, CA: The pressure to make the best judgments possible based on real-time data insights is higher than ever. Control engineers are typically in charge of putting the systems in place to make this happen. If you employ PC-based control systems, there are techniques to apply Big Data analytics that aren't too difficult for PLC programmers. As these platforms have matured, the boundaries between what automation controllers accomplish in machines and plants have shrunk. As early as the mid-1990s, a single PC-based controller could combine the functions of a PLC, motion controller, and a graphical user interface. Multiple hardware, software, and networking platforms are no longer a source of costs and inefficiencies. Today, a single industrial PC may serve as an IoT (Internet of Things) gateway, edge computing device, and analytics platform.

While edge computing often deploys analytics on machine controllers, additional analytics code built in the same environment can operate concurrently on cloud services like Microsoft Azure or Amazon Web Services (AWS). Scalability is ensured through communication technologies like MQTT and OPC UA.

There are numerous advantages of running analytics software on the machine controller as a supplement to cloud-based standalone solutions. On the other hand, control engineers' knowledge may not yet be extensively overlapping with the latest IoT technologies making their way into manufacturing. Engineers will shorten their learning curve and increase the odds of successful implementations by using analytics tools in the same engineering platform as PLC, motion control, and HMI when many are launching pilot projects for their first true IIoT (Industrial Internet of Things) and Industry 4.0 concepts. This safeguards machine builders' and manufacturers' IP (intellectual property) without handing up a new revenue stream or competitive advantage to a third party.

Analytics can operate within machine control code for online and offline analyses using PC-based control technologies without missing any connectivity capability that a colossal tech business would otherwise provide. A user-friendly software workbench is used to create graphical analytics sequences. These sequences can be converted to IEC 61131-3 languages, making the code easier to comprehend for PLC programmers and allowing the analytics sequences to run in the PLC for continuous monitoring. PC-based control systems, on the other hand, may quickly embrace computer science and IT programming tools like C/C++ and Visual Studio, as well as local edge technologies like Azure IoT Edge. This can be expanded to cover any other PC-based software platform. In addition, if needed, MatLab/Simulink can be used to augment analytics applications for machine learning on PC-based platforms. Handling as much engineering work as feasible in one environment, regardless of the tools required for the project, is a clear advantage for more effective development.

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