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Nobody likes doing work that isn’t needed, since it consumes time and money with no discernable return. In an ideal world, we would perform the minimum amount of maintenance required to maximize performance or uptime. But how do we figure out when the perform that maintenance? Big data provides a better answer.
Predictive maintenance isn’t new. Mechanics have been doing it since they started working on machinery. But thanks to better sensors, bigger storage, and smarter algorithms, organizations are able to monitor machinery and predict impending failure with levels of precision that were previously impossible.
One of the companies finding good returns with predictive maintenance is Mondi, the global manufacturer of wood, paper, and plastic packaging products. The company, which runs more than 100 sites around the world, worked with analytics software company MathWorks to develop a predictive maintenance application for a plastic extruder machine in Germany.
Every time the plastic extruder machine has an abnormal shut down, it costs Mondi at least €50,000 in cleanup costs and lost revenue, according to Philipp Wallner, an industry manager with MathWorks in Munich, Germany.
“If they let the machine run to failure….they have to stop the entire line, get the remaining plastic out of the line, and clean everything, so that can easily take them a couple of hours and cost a lot of money,” Wallner said. “So they try to avoid it.”
MathWorks was brought in to analyze data with MATLAB and create a digital model of the machine using its Simulink software. When MATLAB detects that there could be a problem – which can happen up to a half-hour before the machine breaks – the software alerts the machine operator via a GUI dashboard, and the operator can take action to avoid an abnormal shutdown.
The MathWorks software has helped Mondi reduced operating costs and the amount of waste generated by the machine, said Michael Kohlert, head of IT and automation at Mondi, which recorded about €7.5 billion in revenue in 2018.
“Our controller calculated savings of €50,000 to €80,000 for one processing problem we analyzed in this case,” Kohlert said in a video on the MathWorks website. “We want to solve about 90 more problems at our plant, directly at the machines. We have a potential of about €2 million per month to reduce. We are about to continue our cooperation with MathWorks to finalize this project for 60 more machines and lowering waste at our plant and our machines.”
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