Gopakumar Introduces Helios’s Failure Prediction Capabilities Unplanned downtime costs industrial manufacturers an estimated $50 billion per year. Nowhere is the problem
lems and reroute production to prioritize critical projects and avoid delays. “By analyzing thousands of data points per second, Helios is able to see patterns in machine behavior and performance that are otherwise undetectable to even the most experienced maintenance professionals,” Gopaku- mar explained. “These behaviors, which may take the form of idiosyncrasies, vibrations, noises or shudders, are moni- tored and analyzed by machine learning to create patterns and draw conclusions about potential downtime with high accuracy and very short lead time.” During the initial study, Helios reported that its technol- ogy successfully predicted 74 percent of machine break- downs. But they stress that that’s only a starting point. “The platform gets smarter, faster, and more accurate every day as it takes in more data,” said Gopakumar. “These progres- sive solutions generate increasingly accurate downtime predictions and, in turn, improve uptime and ROI.” Brian Kentopp, Helios’s Vice President of Sales, not- ed that a generation of machine technicians are retiring, carrying decades of knowledge and experience about the day-to-day operations of converting equipment out the door. Much of that knowledge has never been recorded or systematized – at best, it resided in the instincts and gut feelings of experienced machine operators. With the labor market as tight as it is, it’s difficult for box plants to find new technicians with the qualifications
more urgent than in the corrugated business, where producers have been pushing equipment – both old and new – to its limits in order to meet surging demand. That’s why Gokul Gopakumar, Senior Director of Development and Data Science at Glen Arm Maryland
Gokul Gopakumar
based Helios IIoT, is betting that their new failure predic- tion capabilities could save box manufacturers as much as $122,000 per machine per year. Helios is an OEM-agnostic machine learning IIoT plat- form that provides insight into corrugated machine perfor- mance. This year at Corrugated Week, Gopakumar pre- sented the company’s new failure prediction capabilities – software that allows manufacturers to not only monitor machine performance in real time, but to view an ongoing assessment of the likelihood of failure or interruption with- in a 30-minute window. This new machine learning algorithm provides oper- ators with actionable insight into the well-being of their equipment. This data can help them forecast future prob-
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