Gopakumar (CONT’D FROM PAGE 18)
This gives operators the chance to make decisions in advance before an interruption occurs. If a machine is operating at high risk of failure, operators can stop it in advance to investigate the cause before a disruption. Managers can also reroute work ahead of time for high priority projects or alter production schedules accordingly to avoid a break-down. “There is also more to machine learning than simply detecting anomalies,” said Gopakumar. “All usage data that is recorded and reported from each machine can be not only aggregated and seen in real-time, but can also be analyzed historically, allowing operators and supervisors to make data-driven decisions regarding quality and fully optimized operations.” Typical maintenance schedules require servicing on fixed periods, regardless of actual usage, but they don’t help operators understand what’s actually happening in- side the equipment. With Helios’ sensors collecting re- al-time information, box plants can determine how much a machine actually ran since it was last maintained: what was the load, what was the burn rate, etc. And by moni- toring new equipment right from the start, operators can track degradation paths over time, monitoring how the machine begins to vary from the OEM specifications. In order to generate its predictive models, Helios needs two distinct sets of data. The first is the ongoing raw in- formation provided by the sensors Helios installs on the
to step in. It could be years before new machine operators get the experience to develop the right instincts. “So many corrugated plants rely on human intuition and experience to drive their decisions,” added Kentopp. “But the information produced by corrugated machines is often very challenging for humans to understand with the naked eye. If humans are using this data at all, they are usually doing so retroactively to try to diagnose the root cause of a problem after it has already occurred. But machine learn- ing has the power to turbocharge each of these same data points and to turn them into actionable intelligence that can be used proactively for decision making, or even to predict and prevent problems before they happen.” This kind of machine-generated prediction tech used to be purely theoretical. But Helios reports these machine learning models are now fully operational and live on the platform, ready for customers who have subscribed to the company’s full suite of features. Using current sensor information along with historical downtime data, the He- lios platform provides an up-to-date picture of operating equipment and actionable information about future func- tioning so operators can make proactive decisions. “The algorithms are no longer simply providing diag- nostic information about past machine performance,” said Gopakumar. “They’re providing a picture of ongoing and forecasted future performance.”
CONTINUED ON PAGE 22
YOUR PARTNER OF CHOICE CONVEYOR SAFEWALK Increase Safety and Reduce Injuries • Reduce or eliminate roller related injuries • Compatible with all conveyors • Simple, adaptable controls package
• Push button for safe walk pause/resume • 48"/60"/72"/84"/96" widths available • Steps with handrail optional
WWW.INSPIREAUTOMATION.COM │ 800.578.1755
20 October 17, 2022
www.boardconvertingnews.com
Made with FlippingBook Annual report maker