Use IIoT to improve operations

Data analytics for IIoT: More data is just more data. Data analysis software is the key to extracting insights and creating value from the Industrial Internet of Things (IIoT) opportunities in production facilities. See two implementation examples and the four needs of data analytics.

03/09/2016


Figure 1: Getting the most from available information requires collecting data, transmitting it via wired or wireless methods, analyzing it with software, and making it available to engineers via smartphones, tablets, and computers. Courtesy: SeeqThanks to a new generation of wired and wireless sensors, data can now be economically generated and gathered in quantities never previously available and then sent to process control and monitoring systems via plant networks or through the Internet. Data can then be used to improve automated real-time control and to help plant engineers and operators make better decisions regarding operation and maintenance. It is also available to data analysis software, which can be used by plant personnel to increase efficiency, diagnose equipment problems, and improve safety.

As a result of this opportunity for new insights, terms such as the Internet of Things (IoT), the Industrial Internet of Things (IIoT), big data, and Industrie 4.0 are now common. Recent advances in sensors, connectivity, and data analysis software combine to make it easier and less expensive to acquire, send, store, and analyze information. But no matter the terminology, the objective is the same: better insights faster. 

Green and brownfield applications

For the sake of simplicity, there are three common scenarios in which the IIoT, to choose one of the terms, can improve plant operations. They may be summarized as brownfield, greenfield, and servicization. All three scenarios can be described independently, but will frequently co-exist within the same plant.

Brownfield refers to existing plants and operations where new sensors are added to existing control or plant networks. Common brownfield scenarios include adding a wireless system and sensors to expand operator visibility and asset monitoring capabilities or adding sensors to replace the eyes and ears of engineers being transitioned to centralized remote monitoring centers or integrated operations facilities.

Greenfield scenarios are deployments in plants or facilities just coming online with IIoT projects. This is the most common scenario for smart city or public sector projects, and it's where the association of IIoT with cloud-based monitoring systems originates because the project isn't designed around an on-premise control and monitoring system. The key difference with most greenfield deployments is this lack of a control system infrastructure, but there are many examples of these systems monitoring remote tank farms, pump stations, and vehicles as a complementary system for an existing facility.

Cash in on services

Finally, "servicization" is one of several monikers describing the inclusion of a remote monitoring capability for an asset. This is the future of your personal vehicle-unless you already have a Tesla and your vehicle information is piped continuously back to the dealership-and of many assets in process plants. Vendors of pumps, valves, and many other asset types are introducing subscription services for monitoring equipment installed on customer promises. The business benefit is asset reliability and uptime, but the real driver is the opportunity for the vendor to provide expertise in asset performance and management.

All of these models follow a common architecture of sensor, communications network, and analysis that is very familiar to the process industry and noted by many prominent industry speakers. Even the difference between the brownfield and servicization models may be summarized as a question of where the data lands for analysis-at the plant or with the asset vendor.

But familiarity with the IIoT architecture and vendor advocacy over what is or isn't a part of the IIoT architecture—cloud systems, fog computing, big data, and Internet standards being the most common examples—should not obscure the fact that there are new opportunities for improved plant performance enabled by these new technologies, and available at drastically lower price points.

The key question for manufacturers with existing plants then becomes: "How do we bring our facilities forward into a smarter state?" The answer may require any or all of the IIoT deployment models mentioned, but should always be framed in the context of the end benefit: better insights faster. 

Start with sensors

Sensors are the starting point in the data collection process. They monitor operation of the "things" in the IIoT: pumps, valves, and other assets. Their cost of implementation and use is dropping rapidly, making it cheaper to acquire more data. Plant personnel were once limited to 4 to 20 mA, HART, or various fieldbus protocols to connect these sensors to control and monitoring systems and software. But today, they can use many types of wired and wireless data connection methods, often employing multiple networks simultaneously in one plant (see Figure 1).

These sensors and connections are what enable new data from new sources to be accumulated quickly and inexpensively, and, as shown, there's a wide range of modern networking options for deployment. Battery-powered transmitters require no signal or power wiring infrastructure, so they can be installed in locations far away from a process unit's wired signal termination points. They can also operate safely for years in hazardous and other areas.

Wireless instrumentation also makes it possible to monitor a wide variety of equipment and systems previously too difficult or expensive to reach with wired solutions, such as 4 to 20 mA or fieldbus. Pump monitoring is an excellent example because these assets are found in most every process plant in large numbers and because their reliable operation is critical. 

Learn more about brownfield IIoT monitoring and the four requirements for data analytics.


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