Predictive maintenance innovations are allowing manufacturers to avoid parts supply headaches before they happen.
That’s according to PMMI Business Intelligence’s 2022 report “The Future of Automation in Packaging and Processing.” This news is especially beneficial for packagers, as the report notes 70% of consumer packaged goods (CPG) companies say their packaging machinery is at least slightly more likely to experience downtime than other machine types.
Regular and routine maintenance to keep machines running and avoid costly downtime has historically been the order of the day, but this does not stop machines from eventually grinding to a halt as parts wear out.
“Usually we don’t find out anything is wrong until the thing breaks down and then a line is down and we’ve got people standing around and our maintenance guy is running around like crazy trying to fix it,” one CPG representative said in the report, highlighting the need for some predictability.
Data analytics have been used to assess operational conditions for years, with many setups connected to historians to track machine performance. But many more are not connected, and measurements collected by existing infrastructure are not necessarily the ones needed to be most effective at performing predictive maintenance.
Smart sensors spot the unexpected
Enter “smart sensors,” an emerging category of hardware disrupting the status quo of predictive maintenance.
Smart sensors are typically small micro-electro-mechanical systems (MEMS) devices with integrated wireless communications and microprocessors, designed to be placed on existing infrastructure creating new points of asset measurement.
They have emerged in the last three to four years largely as a result of MEMS technology dropping substantially in cost thanks to its broad uptake in consumer applications. What was cost-prohibitive before is no longer so, and assets can be retrofitted and monitored in a way never thought possible.
Data collection and analysis advancements like smart sensors help CPGs understand where bottlenecks can appear in production lines and what the causes are for downtime, enabling significant jumps in efficiency.
OEMs and integrators rank use cases for predictive maintenance
With advancements in predictive maintenance technology allowing for such a wide range of data collection, original equipment manufacturers and integrators are considering the most useful areas to watch, as surveyed in PMMI Business Intelligence’s 2021 report “Packaging and Predictive Maintenance.”
Respondents collectively picked run-time as the best candidate for predictive maintenance and data collection, with over 70% saying it would be extremely or very effective. All respondents said it would be at least slightly effective to track run time.
OEMs and integrators cite run-time as the most useful data-type to gather with predictive maintenance.PMMI Business Intelligence, 2022 The Future of Automation in Packaging and Processing
Also prime for predictive maintenance is current or voltage draw. Over 60% of respondents said the technology would be very or extremely effective, and again, all agreed it would at least be slightly effective.
A similar share (just over 60%) of respondents said asset temperature would be a very or extremely effective metric to track with predictive maintenance. Just under 10% of respondents disagreed, though, noting asset temperature monitoring as not effective at all.
Other metrics over 50% of respondents noted as at least very effective include vibration, speed, and pressure.
The lowest-rated specific machine metric for predictive maintenance was magnetic field, with about 15% of respondents considering it very effective or better. Nearly 30% said it would not be effective at all.
Magnetic field distantly trailed behind the next candidate, acoustics. About 30% of respondents said predictive maintenance would be at least very effective for acoustics, with just over 10% noting it as not effective at all.
Source: PMMI Business Intelligence, 2022 The Future of Automation in Packaging and Processing
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