How to Implement Analytics and Stay in Control of Your Big Data

March 21, 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.

Demands to reach the best decisions based on real-time data insights are greater than ever. The responsibility to apply the technologies to make this happen often falls at the feet of controls engineers. Fortunately, there are ways to implement Big Data analytics that aren’t outside PLC programmers’ comfort zone—if they use PC-based control systems. As these platforms have evolved, the walls have come down in terms of what roles automation controllers play in machines and plants. As far back as the mid-’90s, one PCbased controller could combine the functionality of PLC, motion controller, and HMI. This eliminates the previous costs and inefficiencies from multiple hardware, software, and networking platforms. Today it’s possible for one industrial PC to assume the roles of IoT (Internet of Things) gateway, edge computing device, and analytics platform.

While deploying analytics on machine controllers is more typical in edge computing, additional analytics code developed in the same environment can be run concurrently in cloud services, such as Microsoft Azure or Amazon Web Services (AWS). Communication standards such as MQTT and OPC UA ensure scalability.

There are many benefits to running analytics software on the machine controller as a supplement to standalone platforms that run in the cloud. However, the expertise of controls engineers may not heavily overlap yet with the latest IoT technologies finding their way into manufacturing. By applying analytics tools in the same engineering platform as the one for PLC, motion control, and HMI, engineers will shorten their learning curve and boost the odds of successful implementations when many are launching pilot projects for their first true IIoT (Industrial Internet of Things) and Industry 4.0 concepts. This also protects the IP (intellectual property) of machine builders and manufacturers without giving away a new revenue stream or competitive advantage to a third-party.

Using PC-based control technology, analytics can run within machine control code for online and offline analyses and not miss any functionality or connectivity that a big tech company would otherwise deliver. Graphical analytics sequences are developed in a simple-to-use software workbench. These sequences can be converted into IEC 61131-3 languages so code is easy to understand by PLC programmers and ensure that those analytics sequences can run in the PLC for 24/7 monitoring. Fortunately, PC-based control systems can just as easily adopt computer science and IT programming tools such as C/C++, Visual Studio, or use local edge tools such as Azure IoT Edge. This can be expanded to include any other software platform that runs on a PC. In addition, PC-based systems can incorporate MatLab/Simulink to enhance analytics applications for machine learning, if desired. Regardless of the tools needed for the job, handling as much engineering work as possible in one environment is a solid advantage for more efficient development.

While the toolbox is almost limitless, manufacturers that have implemented applications with this kind of PC-based control technology do not need any new tools to run the appropriate analyses. With accompanying configuration tools, users of analytics toolsets offered in PC control can comfortably sift through the data as it is cyclically acquired by analytics loggers.

Available software libraries contain function blocks for several types of cycle analysis such as: data classification; minimum, maximum and average cycle times; and value integrators. They also contain function blocks for threshold value monitoring, providing the ability to document the number of threshold value violations. Other function blocks can analyze signal amplitudes and store indicators like maxima and minima. Many different variables can be selected from a large data package to graphically display them, for example, with a post-scope configuration using software-based scope tools. The configurator also provides algorithms from the analytics PLC library to examine data offline for limit values or to perform runtime analyses of machine cycles. The total running time of a machine cycle—the shortest, longest, and average running times—can be easily determined. The results of important data can be displayed on dashboards for the machine HMI and other devices.

When surveying IoT solutions available in PCbased control architectures, PLC programmers can create new platforms or retrofit existing systems to crack the Big Data puzzle. This can be done without losing control of a major aspect of modern controls design or by adding layers of complexity from standalone systems.

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