Eliminate Overspending On Maintenance

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?

Target maintenance more effectively

Senseye PdM is at the leading edge of using advanced machine learning for condition monitoring. Its unique proprietary algorithms can turn data into an accurate forecast of the remaining useful life (RUL) of manufacturing assets – a technique known as prognostics.

Senseye PdM connects to existing data sources while normal machine operation continues as usual. Over 14 days it operates in the background to analyze normal machine behavior as well as historic data if available. It’s then ready to provide the insights needed to start building a scalable Predictive Maintenance program.

This means in practice that Senseye PdM can tell you how every asset on your site is performing at any given time by automatically gathering and analyzing machine data. The solution’s algorithms can generate updates for individual assets and highlight exactly where maintenance teams should be focusing their efforts in the short-term, as well as helping to optimize any plans for future maintenance.

Typical implementations can cut downtime in half, deliver a 55% increase in productivity and boost maintenance accuracy by 85%.

It’s this last figure that relates most closely to a reduction in over-maintenance, which is one of the key benefits that together enable Predictive Maintenance to save between 8 and 12% of maintenance costs compared to planned maintenance1. And, with typical industrial companies spending between 15 and 40% of their ongoing budget on maintenance, that’s a huge saving2.

Want to find out more about how Sensey PdM can help optimize maintenance spending and boost productivity? Check out our white paper ‘Eliminate Overspending On Maintenance' or book a demo of Senseye PdM today.

  1. U.S. Department of Energy
  2. Lofsten, 2000