Predictive maintenance & condition monitoring
PREDICTING VS. PREVENTING: WHAT IS BEST?
W
ith manufacturers constantly adjusting to cope with rising competition, rapidly changing consumer trends and pressure
to deliver high-quality products quickly, equipment downtime can seriously compromise a business’ bottom lines. According to a report published by maintenance specialist Senseye, manufacturers experience an average of 27 hours of downtime per month due to equipment failure, resulting in yearly revenue losses in the multi-millions.
PAST VS PRESENT 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 guidelines and historical data to give plant managers the best chances to keep machines in good repair but still requires cyclical planned downtime. However, the Operations & Maintenance Practices Guide guide also reports that predictive maintenance is estimated to save companies around 12 to 18 per cent in costs compared to reactive maintenance. Predictive maintenance, on the other
When it comes to industrial equipment, emergency repairs are known for extending unplanned downtime. Both predictive and preventive maintenance are helpful to keep industrial equipment in good condition and to tackle problems before they occur. However, there is a big difference between the two. Here Neil Ballinger, head of EMEA at automation parts supplier EU Automation, offers an overview of the pros and cons of both methods.
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hand, 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 to 30 per cent compared to other maintenance methods.
STORING BIG DATA To work effectively, predictive maintenance relies on data from sensors that report on equipment’s health state. However, IBM estimates that about 90 per cent 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
October 2022 Instrumentation Monthly
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