w FEATURE Machine Safety
To predict or to prevent – what’s best?
Neil Ballinger, Head of EMEA at EU Automation, offers an overview of predictive and preventive maintenance methods in keeping industrial equipment and hence plants in good condition
A
report published by maintenance specialist Senseye found that manufacturers experience
an average 27 hours of downtime per month due to equipment failure. Needless to say this results in revenue losses running in the millions. So, what’s the best way in keeping these downtimes down – by preventing or predicting them? Preventive (or preventative) and
predictive maintenance are often used interchangeably in reference to maintenance strategies that allow manufacturers to act before equipment fails. Both methods are vastly superior to reactive maintenance, where equipment runs until emergency repairs are needed, often costing companies four to five times as much as proactive maintenance options, as reported by Operations & Maintenance Practices Guide, Release 3.0. However, preventive and predictive
are not the same. Preventive maintenance involves checks at regular intervals, regardless of the equipment’s condition. It relies on best-practice
30 April 2022 | Automation
guidelines and historical data to give plant managers the best chances to keep machines in good repair but still requires cyclical planned downtime. The Operations & Maintenance Practices Guide reports that predictive maintenance is estimated to save companies around 12-18% in costs compared to reactive maintenance. On the other hand, predictive maintenance only occurs when needed, relying on real-time data from IIoT- connected equipment to identify potential threats before a problem occurs. In this way, repairs address an actual problem and are more targeted, meaning that downtime, when required, is reduced by 25-30% compared to other maintenance methods.
Utilising big data To work effectively, predictive maintenance relies on data from sensors that report on equipment’s health state. Sadly, IBM estimates that about 90% of all data generated by sensors goes unused. This means that manufacturers miss opportunities to make informed decisions about their equipment while
still paying to collect and store data. Data collected but not processed or used in any way is known as “dark data” and represents a sizable challenge for the industry.
For example, current transformers can collect raw data on an electric motor’s eccentric rotors, winding issues, rotor bar issues and more. However, if there is no software such as CMMS to organise the data, this information might not be shared with the maintenance team on the production floor. This could result in unplanned downtime despite the use of sensors.
While sensors to collect data can be
relatively inexpensive and easy to set up, the challenging part is processing that data to make conclusions on the health of the machine. Processing data can be challenging for many reasons, from understanding it to placing it with the relevant department. For example, data silos happen when data is processed and relevant patterns discovered, but these are not shared among the different departments of an organisation. This can happen due to the business not having the necessary
automationmagazine.co.uk
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