Predictive maintenance is the science of knowing when to apply maintenance techniques to your equipment, based not on guesswork or set time intervals but careful measurements made with monitoring tools.
In general, there are three kinds of maintenance:
- Reactive maintenance: Repair it when it breaks. (Example: a kid’s bike.)
- Preventive maintenance: Replace before we think it will break. (Example: automotive fluid changes)
- Predictive maintenance: Monitor, measure, and base decisions on actual performance and expected fail probability.
The problem with reactive maintenance is that typically things break when you don’t want them to, leading to a lot of downtime and production loss.
The problem with preventive maintenance is that you are wasting parts and doing unneeded replacements, while still running the risk of things breaking when they wear more than expected.
For example, consider a starter engine for a car. You could decide to replace this every 100,000 miles to avoid breakdown due to wear. If one person starts the car 100,000 times to drive one mile each time, the wear will be far greater than if that same person drives 500 miles every time he starts the car. Things like heat, climate and other factors also influence this.
Predictive maintenance is far more scientific, because it is about measuring and comparing. Instead of replacing the oil in a machine, you may take an oil sample and analyze it. If it’s still good, you don’t need to replace it. The time and cost of the analysis was less than the time and cost of replacement.
When applying predictive maintenance measures, you will measure things like energy consumption, vibration, temperature, and heat to see how moving parts like bearings and gear boxes operate. You may also measure chain stretch to predict when a chain will be at the end of its service life.
There is a challenge inherent with predictive maintenance, and that is the switchover. Many companies may feel like they have to implement it the same way they would a major project. They will buy software, measure baselines, enter maintenance points, create work sheets, collect data, etc. But many maintenance departments are understaffed and are typically running from crisis to crisis, so they don’t have the resources to do this. Management may also not be willing to invest in this, because the pay back is half a year down the line and hard to quantify.
Interflon is also developing a fourth regime, called Pragmatic Maintenance. Rather than trying to improve the maintenance of the whole plant, just start with the mission critical components and leave the rest as is.
You are the maintenance manager at a facility that cuts marble countertops. You have one cutting machine and 50 hand-held buffers to finish the countertops. You can have a predictive maintenance regime on the parts of the cutter that stop production if they fail, but still practice reactive maintenance on the buffers. These cost just a few hundred bucks, and by buying a few spare ones, you don’t need to spend a lot of time administering all kinds of things regarding them.
Our message to you about predictive maintenance is this: you can grow towards a predictive maintenance regime without a Big Bang that puts half your team into a burnout.
Have questions about how to implement predictive maintenance techniques at your business? Give us a call or drop us an email. We are only too happy to help you save money!