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Collection and Analysis of Reliability Data
Having an in-house reliability data bank can help a company assure a facility's safety and environmental integrity, increase production, reduce maintenance effort and build better (new) facilities. Companies can also demonstrate to whichever Regulator, that they are responsibly run with proper controls and a full audit trail. In-house data is of better quality and more applicable to your operating context than any generic data source. It is easier to verify and hence more believable.
What can be done with this data?
For a start, bad-actor analysis, to focus effort on eliminating unreliability of the worst performers. With only a reasonable volume of data, simple parameters such as Mean Time To Failure (MTTF), Mean Time To Repair (MTTR) and other failure distribution parameters can be calculated. These information are useful for:
Carrying out RCM studies,
Bench-marking and best practice transfers,
Design improvements,
Reviewing current maintenance routines.
You can start making a performance difference very quickly, at relatively low cost.
How to collect and use reliability data?
Many organizations think data collection is too much hard work for too little reward. However, a well-organized company with motivated people can make data collection so easy it won't even feel the effort. The key is to appoint a Reliability Focal point reporting directly at senior management level thereby underscoring managements' commitment to the reliability drive (see last month's Performance TiP for more on this).
Identify what data is needed to compute equipment reliability statistics. Consider:
Equipment identification or Tag number. If the item can sit in more than one location, its unique identification number as well,
Dates are vital, namely start, stop, standby,
Clear performance standards and failure definitions so everybody understands and agrees on the criteria used to define failure.
These are all that is required for the basic reliability work, but with very little extra effort other useful data could be collected, such as:
Severity of duty-steady, start/stop, cyclic or variable loading, operating close to or well below duty point.
Loading roughness information is very useful,
Operator's idea of what failed - this may later turn out to be incorrect, but record it anyway. Once examined further, record actual reason.
What was found wrong when opened up - fouled, broken, worn, misaligned, burnt etc.
Recording as-found condition is important. What may have caused failure or what did cause it, should eventually be recorded.
If you'd like more information on data analysis for better operational decisions please e-mail info@snoino.com.
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Thanks to V. Narayan for his contribution to this month's Performance TiP.
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