Engineering changes are always a trade-off that adds good behavior at the expense of some bad behavior. The question is, does the good outweight the bad?
For example, the following table identifies a few common metrics and their possible undesirable impacts:
|Additional Testing||FD / FI||Diagnostic or Maintenance Time|
|Serial Replacement||Fault Group Size||Maintenance Time|
|Add Redundancy||Safety, Critical Behavior||FD/FI, Maintenance, Reliability|
|Add Sensors||FD / FI, Maintenance||Reliability, Maintenance|
|Preventative Maintenance||Reliability, Availability||Support Cost, Maintenance|
Of course, some changes are far more complex. Repartioning, for example, can have any number of good and bad consequences. Often, it is highly efficient to repartition for diagnostic purposes (better detection, isolation, etc.) However, what happens to maintenance? By the same token, the partioning could be done specifically to improve maintenance. But then how is diagnostics changing to accomodate it?
Problems such as these are what some System Engineers live for, and they are also what can make and break a process or tool, and ultimately the entire project. Today, Prognostics often opens Pandora’s box for these same reasons.
Prognostics can be used to extend useful life, but that can mean sacrificing reliability. It can also improve reliability through advanced warning, but that can mean losing some availability. Determining the overall benefit of these opposing factors is what triggers most engineers to start thinking about risk. The degree of accuracy to which these factors can be determined is real hurdle in assessing the true risk of producing a supportable design that meets requirements.
Not to be confused with safety, engineering risk reduction is a measure of the likelihood that the engineering effort itself will be successful. Trade-off studies can be a huge source of risk that can go unnoticed if there are flaws in the studies themselves.
As pointed out in part one, prognostics and partitioning are two techniques that are often not entirely calculated out in terms of their overall contribution towards risk. This is largely because their effects tend to be realized as part of longer term deployment, than the more classical approaches which are more easily measured.
Take, for example, preventative maintenance. A look at reliability, duty cycles, modes of operation, etc. are sufficient to determine how preventative maintenance should be added to provide improved availability or safety. And proof is easily realized through similarity analysis or initial field results. Furthermore, it is often easily modified should problems be identified.
Prognostics and partitioning are up-front, very fundamental design decisions that are made specifically to improve operational behavior, especially support. This impact that stretches far out into the support arena is the biggest cause of uncertainty with these type of decisions.
The solution, is simulation. Simulation provides the ability to look far into the future–at least as far as the support concept can be theorized. It provides the full feedback from long-term support costs back to the fundamental design decisions.
The pressure this places on today’s engineering tools is why simulations are becoming more commonplace. The eXpress tool is not unfamiliar with simulation, having been pioneered back in 1997 (for an IEEE AutoTestCon presentation). Today, eXpress continues to be influenced by these concerns as the balance betweeen diagnostics and prognostics is explored with increasing frequency.