SEP-OCT 2018

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INTECH SEPTEMBER/OCTOBER 2018 37 AUTOMATION IT Processing stream data, especially at these rates, is a high load on real-time systems. One of the design objectives should be to remove any unneeded cal- culation and processing from this layer. A comment that includes the phrase "computers are fast, and memory is cheap" is a giant red flag that there are problems in the architecture. The next step is to design simple, discoverable microservices to manage exchange of data between modules. Discoverability is important, because it allows apps to automatically attach to the data and its history to support a "plug and play" design—an essential requirement in large systems, where building with the expectation of manual configuration limits scale. Transparency There can be quite a lot of information generated from sensors and other com - puters (e.g., NOAA Weather for a power grid). Often, a curated view is provided to al- low better interpretation of the information. The Industry 4.0 standard specifically addresses the need to have models to provide this added con- text. While the complete function is an application, managing the shaping or metadata is an essential part of the infrastructure, so that different applica- tions can present similar views. Although part of the digital twin definition, shaping is also used for reporting, viewing, and production calculations, a s well as models. When implemented online and used for operations, the digital twin requires the same level of technical support as the streaming data infrastructure. Technical support Unsupported software quickly becomes unus - able. Technical support includes bug and error fixes, new logic, security updates, and a "help" function. Layering the parts of Industry 4.0 and defining the service architecture pro - vides the base requirements for service- able software and addresses the need for both interoperability and an open infrastructure. Finally, the system needs to be designed to be resilient in the face of abnormal events, whether caused ex - ternally (e.g., an exploit from a hacker), internally (e.g., fault or error in logic), or by a standard procedure (e.g., a system update). This often results in the use of high availability or redundant systems. In addition, the infrastructure needs to be monitored for detectible faults. Decentralized decision making The final requirement of Industry 4.0 includes two requirements for the ar- chitecture. I would like to emphasize caution on this: Automation should be done by a control system designed for the task. Most articles that extoll the value of having IoT start your car, open your door, or take other actions have not sufficiently addressed the potential for abnormal events that could bring harm to people or equip- ment. The same is true on alerts and alarms. At one of the American Petro- leum Institute committees on alarms, a user noted that there are multiple cases in a refinery where it is safer to burn down a heater than shut it down. The procedure for handling alarms is thus more complex than it appears and requires knowing the larger con- text of the action. In another example, one cause of the Northeast blackout of 2003 was the transient caused by the protec- tive equipment. From an architecture perspective, the best protection is to provide all the information needed, a robust system, and tools for imple- menting automation. My belief is that control belongs in the on-premise equipment designed for this task. Many of the new efforts to use com- puters to improve the management of industrial processes, variously called big data, Internet of Things, artificial intelligence/machine learning logic, Industry 4.0, or digital transformation, are merging into an approach that Professor Michael Porter called the "system of systems." No individual system or piece of equipment has exclusivity on the use of digital computation, from lowest (e.g., the hardening of a sensor or smart me- ter processor) to highest (e.g., better management of the power balance of a country). The only difference between a small system and a large one is scope, and to have the proper scope, the sys- tem of systems must scale. n ABOUT THE AUTHOR J. Patrick Kennedy, PhD, the founder and CEO of OSIsoft, has been at the fore - front of bringing digital technology to the energy industry for more than three decades. Contact Michael Kanellos ( with questions or comments. View the online version at Source: Wikipedia Interoperability: the ability of machines, de vices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP). Information transparency: the ability of information systems to create a virtual copy of the physical world by enriching digital plant models through the aggregation of raw sensor data into higher-value context information. Decentralized decisions: the ability of cyber-physical systems to make decisions on their own and to perform their tasks as au- tonomously as possible. Only in the case of ex- ceptions, interference, or conflicting goals are tasks delegated to a higher level. Technical assistance: first, the ability of assistance systems to support humans by ag- gregating and visualizing information com- prehensively for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber-physical systems to physically support humans by conducting a range of tasks that are unpleasant, exhausting, or unsafe for their human coworkers. The four principles of Industry 4.0

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