Manufacturing companies have never needed to make optimal decisions based on real-time data more urgently. The responsibility for achieving this task often falls to control engineers. Fortunately, if they use PC-based control systems, there are ways to better implement big data analytics without straying too far from the comfort zone of programmable logic controller ( PLC ) programmers.
As PC-based control platforms gradually enter the era of the Internet of Things (IoT), the boundaries of the roles played by automation controllers in machines and factories have blurred. As early as the mid-1990s, PC-based controllers could already perform multiple roles, including PLCs, motion controllers, and human-machine interfaces (HMIs). This eliminated previously existing costs and avoided the inefficiencies caused by relying on multiple hardware, software, and network platforms. Now, industrial PCs can act as IoT gateways, edge computing devices, and data analytics platforms.
Running analytics software directly on the machine controller, as a complement to a higher-level, standalone platform running in the cloud, offers numerous advantages. However, the expertise and skills typically possessed by a control engineer may not overlap significantly with the latest IoT technologies applicable to manufacturing environments.
As many companies launch pilot projects for their first true Industrial Internet of Things (IIoT) projects, engineers shorten the learning curve by applying big data analytics tools to the same engineering platform used for PLCs, motion controls, and HMIs, which facilitates successful project implementation. Simultaneously, it protects and enhances machine manufacturers' intellectual property without incurring additional costs to IoT service providers or third parties.
Using PC-based control technology, analysis code can be run throughout the entire machine control codebase for both online and offline analysis without losing any functionality or connectivity. The graphical analysis sequences are developed in a software workbench and translated into IEC 61131-3 language, making the code easily understandable to control engineers and PLC programmers, and ensuring that these analysis sequences can be run on the PLC for 24/7 uninterrupted monitoring. Fortunately, PC-based control systems are compatible with computer science and IT programming tools.
This can be extended to any other software platform running on a PC. Furthermore, PC-based systems can enhance machine learning and optimization analytics applications. These powerful algorithms can also run in real-time with PLCs and motion controls on PC-based platforms. Regardless of the toolkit required to accomplish this work, implementing as much engineering as possible within a single environment is an advantage in ensuring more efficient project development.
Machine manufacturers implementing machine applications using this PC-based control technology do not require new tools to perform appropriate analysis. Using the included configuration tools, users can filter data acquired by the analysis logger using the analysis toolset provided by the PC-based control system.
When examining the application of IoT solutions within PC-based control architectures, PLC programmers can create new platforms or modify existing systems to address big data challenges. This can be achieved without sacrificing the core control functionality of modern control designs, or by adding additional complex layers to standalone IoT and analytics systems.
