Implement modern analytics technologies to stay in control of your data
Demands to quickly reach the best decisions based on real-time data insights have never been greater. Of course, the responsibility to apply the right technologies to make all this happen often falls right at the feet of controls engineers. Fortunately, there are ways to implement big data analytics in ways that aren’t too far out of the comfort zone of PLC programmers if they use PC-based control systems.
As PC-based control platforms have evolved into the age of IoT, the walls have completely come down in terms of what the roles are for automation controllers in machines and plants. As far back as the mid-90s, one PC-based controller could assume the combined roles of PLC, motion controller and HMI. This eliminates the previously existing costs and inefficiencies from relying on multiple hardware, software and networking platforms. Fast-forward to today and it is just as possible for one Industrial PC (IPC) to assume the roles of IoT gateway, edge computing device and data analytics platform.
While deploying analytics onboard machine controllers is more typical in edge computing scenarios, additional analytics code developed in the same environment can also be run concurrently in cloud services, such as Microsoft Azure or Amazon Web Services (AWS). Communication standards that rose first in the world of IT are also at play in manufacturing environments today, such as MQTT, as are standards that are more commonly associated with industrial applications, such as OPC UA. This means that scalability is assured.
All in one: PC-based automation delivers control and Big Data analytics
There are many benefits to running analytics software directly on the machine controller as a supplement to higher-level, standalone platforms that may run in the cloud. However, the expertise and skillset of the typical controls engineer may not heavily overlap yet with the latest IoT technologies finding their way into manufacturing environments.
By applying data analytics tools in the same engineering platform as the one used for PLC, motion control and HMI, engineers will shorten their learning curve and stack the deck in favor of successful implementations when many are rolling out pilot projects for their first true IIoT and Industrie 4.0 concepts. This also protects and enhances the intellectual property of machine builders and manufacturers, without giving away a new revenue stream or competitive advantage to an IoT services provider or other third-party.
Using PC-based control technology from Beckhoff, analytics code can be run within the overall machine control code for online and offline analyses and not miss any functionality or connectivity that would otherwise be delivered by a big tech company. Graphical analytics sequences are developed in a simple-to-use software workbench. With TwinCAT 3 automation software, these sequences can then be converted into IEC 61131-3 languages so code is easy to understand by controls engineers and PLC programmers, and ensure that those analytics sequences have the ability to run in the PLC for 24/7 monitoring.
Computer science standards and simulation technologies
Fortunately, Beckhoff PC-based control systems are available that 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 essentially any other software platform that runs on a PC. Further, PC-based systems can incorporate MATLAB®/Simulink® to enhance analytics applications via Mathworks toolboxes for machine learning and optimization, if desired. These powerful algorithms can also run in real-time alongside the PLC and motion control on PC-based platforms. Regardless of the mix of tools needed to do the job, conducting as much engineering work as possible in one environment is a solid advantage to ensure more efficient project development.
While the toolbox is almost limitless, machine manufacturers who have implemented their machine application with this kind of PC-based control technology actually do not need any new tools to run the appropriate analyses. With accompanying configuration tools, users of analytics toolsets offered in PC-based control systems 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, value integrators, etc. 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 in order to graphically display them, for example, with a “post-scope configuration” using software-based scope tools. The configurator also provides some algorithms from the analytics PLC library to examine the 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 determined with ease. The results of any important data can be displayed on dashboards produced for the machine HMI and in dashboards viewed on other devices (office PCs, mobile, etc.).
When surveying the current IoT solutions available in PC-based control architectures, PLC programmers can create new platforms or retrofit existing systems in order to crack the big data puzzle. Remember that this can be done without losing control of a major aspect of modern controls design or by adding layers of complexity from standalone IoT and analytics systems.
Want to learn more about applying analytics technologies to your machines and systems? Contact your local Beckhoff sales engineer today.
Daymon Thompson is the Automation Product Manager for Beckhoff Automation LLC.
A version of this article previously appeared in Control Engineering.