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  • The Final Say

By Daymon Thompson

Data acquisition. Big data. Industrial Internet of Things (IIoT). These are buzzwords that anyone researching trends in automation, manufacturing, and processing cannot possibly avoid. Once you cut through the hype, there are many tangible reasons why companies should research these ideas when seeking continuous improvement and heightened competitiveness.

These concepts are quickly becoming integral elements when selecting machine control platforms, purchasing equipment, or organizing a production line. Usual questions about how to get started are “What protocol should I use?” and “What cloud platform or analytics software is best?” Companies that find the most success with IIoT projects start by looking inwardly at their businesses. Often, there is a lack of preparation or planning about business goals that will ultimately drive IIoT projects. Therefore, companies must find innovative ways to do data acquisition and analytics within the framework of current systems.

First steps involve identifying the key business and production challenges the technology should solve. The users must identify the critical data to acquire, including direct sensor data; indirect data, such as motor temperature or torque; and derived data, such as overall equipment effectiveness calculations. At this point, companies select data storage, analytics display, and data acquisition tools based on defined business goals and “use cases.” Companies need to make some decisions. Do they have the ability to implement a capable analytics platform in house? Will the new tools need to work with legacy information technology (IT) systems? These questions must be answered at the ground level of a cloud connectivity implementation plan. In addition, securing an acceptable return on investment from the implementation can seem a bit daunting. However, great strides have been taken to address this issue and help extract actionable data points and drive insight.

Data-driven toolbox

For decades, standard PC-based control systems have successfully connected original equipment manufacturers, machine operators, and other end users to the Internet, high-level IT applications, and corporate databases. The connectivity that forms the basis of IIoT and big data has long been an integral component of PC-based systems. Today, they can be efficiently expanded to incorporate more IoT-focused protocols. Industrial PCs and embedded PCs are ideally positioned to break down the barriers to entry for connected concepts, such as IIoT and Industrie 4.0, bringing next-gen technologies to nearly any application.

Analyzing production and process data has also been a standard function in PC-based control systems. However, recent advances as part of IIoT and Industrie 4.0 concepts enable engineers to perform analyses both at the machine and remotely in the cloud. Data analytics tools are within the same programming environment used to write the machine control code, providing a familiar environment to enhance machine optimizations, efficiently gather cyclic machine data, and perform analyses without a large-scale IoT implementation. Use cases can be discovered, such as the ability to use IoT communication protocols and analytics tools when the machine is built and commissioned. This brings a better understanding of the data through deeper error analysis or significantly improves production through machine optimization.

The growth of publish/subscribe (“pub/sub”) and message broker concepts used in several IoT protocols, such as OPC UA and MQ Telemetry Transport, provides new options for devices to securely transmit and receive data from the cloud. The basic pub/sub and message broker approach lets users simply “publish” data to the broker, and the data is then pushed to all “subscribers.” This eliminates the need to configure direct connections between all connected devices before they can communicate with one another. The concept works within a small network, an enterprise network, or even across the world by implementing the broker in the cloud. With this architecture, multiple applications can subscribe to data for display in dashboards or consumption by analytics platforms to gain data insight. This also means that data can be viewed “on any piece of glass,” creating a truly flexible IIoT communications network.

IoT and cloud technologies continue to make inroads into industrial markets, increasing the emphasis on the integration of advanced connectivity in all application areas. Choosing a robust, standards-focused control architecture such as PC-based control today will make this transition much simpler. It is also a high-level, feature-rich solution for all current needs that accommodates future plans for connectivity and other IoT concepts. Beyond the buzz, there are real-world improvements to make today with readily available IoT technologies for those companies with the vision to succeed early in the era of cloud and IIoT.

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