Scheduled preventative maintenance is used to replace or restore an asset at a regular interval. But is such maintenance applicable and if so what is that interval? To answer that, we must examine how the asset (or function) ages.
Frequency Of Failure
If we are to schedule preventative maintenance, it is useful to know how frequently the asset fails. In RCM, we have 2 key metrics for the frequency of failure:
- MTBF: Mean time between failures. The average time between each asset failure. For a fleet of assets, the mean can be taken across all assets in the fleet.
- MTTF: Mean time to failure. The average life of an asset - the average time from installation (or start of life) to failure. Usually used on lifed items such as cured rubber hoses.
With both of the above, it is important to calculate the mean for a given failure mode, not across the entire asset. An asset may fail for many different reasons (why we do a FMEA) - the MTBF and MTTF must be failure mode specific to be of use.
The Gambler's Fallacy
A fair casino roulette wheel has a 50% chance of coming up red and 50% black (let's ignore the greens). An experienced gambler will tell you that you almost never get 8 reds in a row. And they are correct, the odds are well against it - the probability is just 0.0547. So if you see a table provide 7 reds in a row, you know to bet on black - it's a sure bet!
Of course, the next roll is still 50%, the table has no memory. It is important to avoid the Gambler's Fallacy with scheduled maintenance. An experienced maintainer may have replaced hundreds of valves in their career. They have never seen a valve reach 10 years of life. So we should replace all valves that survive to 9 years? If the MTBF is 5 years, does that give us the schedule interval?
Well it depends - it depends on how the asset ages. As the asset ages, does it increase the likelihood of failure? Experience often leads us to think that it does, but analysis does not always bear this out. Most complex equipment does not see a significant increase in failure probability as it ages.
Early-life Failure
Not only does a lot of complex equipment not see an increase in failure probability over time, many items do see an increase of failure probability at the start of their life or after a restoration task - early-life failures. This could be due to manufacturing issues, transport damage or installation problems.
Failure Patterns
To determine if an asset requires preventative maintenance and what the schedule interval must be, we need to understand the asset's (function's) failure pattern. These patterns map the probability of failure in a given time period against the age of the asset. For assets that do not age (do not see an increase in probability of failure over time), we will get a simple flat line. Early-life failures will show as high probability at the start that reduces over time to a flat line. Asset's that age will see a line that increases over time (either a simple linear increase or often a flat line that suddenly sees exponential growth). Some assets can see early-life failures and ageing, showing a graph that looks like a 'bath tub'.
Analysis
Scheduled preventative maintenance should be backed up by an age exploration that proves an increase in failure likelihood as the asset ages. Many current maintenance schedules would benefit by moving scheduled preventative maintenance to on-condition (inspection) tasks or run-to-failure strategies.
Experience only provides hypothesis, without data and analysis!