JAN-FEB 2018

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far more common. Many companies with existing analytics capabilities in in - dustries like finance, logistics, telecom- munications, or ecommerce have been quietly seeking out new data sets to run through their algorithms and forecasting models. What starts as investigations into the Industrial Internet of Things (IIoT) often leads to industrial process improve - ment applications, which enjoy an ap- pealing combination of high value-added use cases and available data. The result is a raft of new entrants into manufacturing from data analytics. For established players in manufac- turing and automation, the forecasting framework from finance, economics, and many other fields is already familiar. That framework has already been widely applied in measurement and test, qual- ity and inspection, and statistical process control (SPC), among other manufactur- ing disciplines. As a result, many manu- facturers are building up their analytics capability in house. Others are seeking close partners, especially from smaller companies with some manufacturing background, as well as programming and forecasting capabilities. Under the um- brella of predictive analytics, predictive maintenance is the most hotly pursued function. The concept is simple: the cur- rent maintenance paradigm is to fix or repair components once they have failed or based on a predetermined preventa- tive maintenance schedule. In the future, predictive maintenance will dictate repair if and only if failure is about to occur. Process analytics here today Although predictive analytics is getting the most attention, a less glamorous area has the potential to be widely commer- cialized very soon. Analytics for process improvement is a logical next step from monitoring, and it avoids the complex- ity of building good predictive models. The 2016 IoT Analytics report uses the more precise but cumbersome category "analytics that support process auto- mation," which was considered far less important than "predictive/prescriptive maintenance of machines" among sur- vey respondents. Compared to predic- tive analytics, analytics for process im- provements and automation also lacks some of the associated jargon. Machine learning, deep learning, neural networks, and artificial intelligence, for example, are critical leading-edge technologies for predictive models. Process analyt- ics, though, could be as simple as pro- cess visibility helping a human operator, technician, or engineer remove tedious or repetitive manual steps. Process vis- ibility is like shop floor monitoring in that it presents data that already existed but may have been hidden to operators, engineers, or management. In custom automated systems, con- tinuous manufacturing or packaging lines, and high-volume production work, it is already common to include current process data to users and man- agement. This data may include de- tailed views of setups, operations, or individual parts and assemblies. Shop floor monitoring, by contrast, empha- sizes a complete and comprehensive view of all equipment and assets. Com- panies are increasingly demanding both detailed process data and comprehen- sive asset management views. In commercial software packages or those integrated into automated sys- tems, process analytics manifest as feedback to operators or improved com- munication between production and en- gineering. For example, a technician may know to periodically remove a filter for visual inspection. The inspection interval may be set at the production station or dictated by engineering, but additional automated data collection and analysis supplements and improves the process. Switching from manual inspection logs to automatically recording filter removal events saves time, improves accuracy, and decreases the number of process steps for the technician. Other application areas for process analytics include supplementing SPC and quality systems and tool path generation and tool selection for ma - chining. In fact, there is barely a line between SPC and process analytics. Data collected for process analytics can simply be additional inputs to improv - ing existing systems. For machining op- erations, linking data from a machine, cutting tool, CAM system, and even the part itself has huge implications for tooling. Established suppliers, new startups, researchers, and end users have all converged on better tool paths and better tool selection as near-term wins for analytics. How analytics will come to market In the near term, automated predic- tions will not replace human reason- ing in the factory. Complex, nonlinear thinking gives humans the edge over algorithms for managing processes. Where predictive analytics shine today is in highly repetitive, high-volume op- erations where one or a few target vari- ables with a big impact can be mod- eled. Temperature and vibration data collected from rotating parts, such as generators or turbines, is well suited for supplying data to predictive mod - els. Those models will eventually adapt to handling more and more variation and work from smaller data sets. Hu- man operators will still manage work with higher variation, but increasingly benefit from analytic insights. Com- puterized tips and inputs to stream- lining processes are already avail- able today; warnings about predictive maintenance concerns—with a human deciding when and how to act—is easy to picture in the not-too-distant future. Manufacturers are more likely to de- velop process analytics applications FACTORY AUTOMATION 20 INTECH JANUARY/FEBRUARY 2018 WWW.ISA.ORG Layering automated data collection on top of existing processes like inspection, cleaning, or routine preventative mainte- nance can remove process steps, streamline production, and increase throughput.

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