Reducing downtime may be at the top of your wish-list when opting for a Predictive Maintenance regime, but there are other big rewards on offer. Don’t overlook the potential benefit of eliminating over-maintenance.
Over-maintenance is built into every planned maintenance program that relies on carrying out jobs at regular, predetermined intervals. That’s because everybody from OEMs to maintenance managers builds a safety margin into maintenance intervals ‘to be on the safe side’.
The high cost of caution
Take the guidelines for replacing a greased-for-life bearing as an example. Recommended replacement intervals can be anywhere between 16,000 and 40,000 operating hours. Each bearing change takes anywhere between 30 minutes and 2 hours. The man-hours spent replacing bearings will be 2.5 times greater at a replacement interval of 16,000 hours, compared with 40,000 hours. There are thousands of bearings on a typical industrial site, so that’s a massive additional cost.
Predictive Maintenance can virtually eliminate this maintenance overspend by enabling teams to carry out maintenance in the nick of time to prevent a breakdown, rather than at fixed intervals. At the same time, it drastically reduces downtime and promises significant productivity gains.
However, Predictive Maintenance is only possible with support from effective condition monitoring, which can reliably predict when each component is going to fail. Condition monitoring relies in turn on taking real-time data from machinery and using it to build a picture of the equipment’s evolving condition.
Game-changing technologies
Condition monitoring has recently become an affordable and practical proposition for the first time in many industries, thanks to the arrival of techniques and technologies associated with Industry 4.0. For example, wireless communication makes it cheaper and less disruptive to install a network of low-cost sensors to feed the necessary machine data into condition monitoring systems. Meanwhile, cloud-based software delivers unprecedented scalability and allows process operators or OEMs to monitor installations in real time.
Better still, a new generation of artificial intelligence and machine learning solutions enable condition monitoring systems to automate the analysis of data, providing actionable insights without the need for expert analysts to pore over every scrap of data. Why pay consultants to try and forecast machine failure when it can now be done automatically, in the cloud and without manual intervention?
Want to learn more? Download our Eliminate Overspending on Maintenance white paper here: https://hubs.ly/H0pH-L10
Contact details:
Senseye
Epsilon House
Enterprise Road
Southampton Science Park
Southampton
SO16 7NS
United Kingdom
Tel: +44 (0) 845 838 8615
Email: sales@senseye.io
Website: www.senseye.io
