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More about Predictive Maintenance

March 1, 2018

Ask any of the operators, or even Thomas, and they will tell you how often our machines break down. The attitude is one of I-can-fix-it, which is commendable, but how much does that attitude cost? Even a ballpark number of time spent on repairs would be in the thousands. By adopting predictive maintenance we'll be trading fix-it time for check-up time. And those check-ups will take far less time.

Pros include-

  • Condition based repairs
  • Less time spent fixing the machine
  • Active monitoring
  • Safer environment

Cons are-

  • Setting aside time to do check-ups
  • Cost of predictive equipment
  • Cost of training
  • Time spent examining data

I'd argue that the cost of doing predictive maintenance is lower because a machine in good condition lasts longer. While predictive methods don't guarantee a condition won’t change, they help us see when that change will occur. Seeing a problem before it becomes a problem is valuable information.

Safety concerns, money saved over the long run, and downtime prevented- these result from practicing predictive maintenance, but is predictive maintenance enough? Can it catch 100% of the potential issues? Preventative practices still need to be set up to supplement this plan.

How do we look at something and know that this way is a better way than another way? How do we prove the effectiveness of our methods?

If other experiences color our perceptions of how we do things how are we to do anything different?

A lax attitude to predictive maintenance will provide lax results. Nothing will come back “right.” It will be slipshod. Bad data standing in place of real and valuable data. Aren’t we wanting to check for bad actors? The machines that will give us results that we think we want without indicating that there is actually a problem going on. This kind of bad data is what leads to bad actors.

Then there is run-to-failure which is like watching a train wreck in slow motion. From the actual working around the machine, to the machine itself. How well it might make parts one day will be wholly different from how it makes parts in two weeks. The operator might complain a little, adjust, and then deal with the problem by doing nothing. They might notice the machine is not operating as well as it used too.

This is why the culture of maintenance must be changed. Developing schedule and regime are tools, but tools are only useful in the hands that know how to use them. truly getting at the heart of maintain a culture of continuous improvement that will not only affect the machines but the people as well.

This is the tricky bit here a figuring out of what some might call the paradox of maintenance. You want to keep your machines in good condition, but you can’t be looking at them all the time for no reason.

Take for example- sampling. It is good to not only schedule when you will sample but take periodic unscheduled samplings as well. Staggering the sampling scan result in catching problems that may not appear during the scheduled sampling. The machine can get used to running in cycles and if you take it out of that cycle it can end up showing other things that are wrong with the machine. Making random check-ups can find issues that can hide during cyclical check-ups. On the other hand, too much sampling won't tell you anymore and will waste a bit of time. So, there is a balance that needs to be worked out.

Using both preventative and predictive maintenance methods casts a wider net. While the initial cost is higher, with a good team and a good plan, the returns over the long run are significant.