## Field-Finds

#### Using MIL-HDBK-1823A methods with Field-Finds can lead to gross errors in POD.

#### Here’s a great idea:

- Rather than build expensive POD specimens, let’s take the parts inspected at depot and set aside those with indications for use as POD specimens.
- We can identify the size that produced the indication and then “analyze” the data using mh1823 POD methods. (We’ll save tons of money.)

#### Why is this *not* such a great idea?

*not*

*Field-finds* are the most detectable cracks in a given size range, since those are the ones your inspection found. It is irresponsible to use these most-detectables to represent all of the cracks because you will grossly over-estimate your inspection’s capability, claiming, for example, 90% POD when the true detectability is closer to 50%.

The statistical properties of Field-Finds are radically different from Probability-of-Detection (POD) demonstration specimens. MIL-HDBK-1823 methods require that you know the entire population of cracks to be inspected. So even if a crack is missed in a POD demonstration, its size and other characteristics are known because it is a laboratory specimen. Special methods (censored regression) can then be used with *known* misses to determine the \(\hat{a} \textit{ vs a}\) (signal vs. size) relationship, and from that the POD(a) function.

But with field-finds you know what you found, *but you don’t know what you missed.* So the methods of ordinary regression and censored regression cannot be used because their underlying requirements are not satisfied. Thus, using **MIL-HDBK-1823**methods to analyze field-finds from an unknown population of cracks will grossly *over-estimate* Probability of Detection for field inspections. This is illustrated in the following figures.

*Figure 1 – MIL-HDBK-1823 Methods Work Well with POD Demonstration Specimens*

The data are 600 simulated observations with the decision threshold set at 80 y-units (\(\hat{a}\)) of which 122 had \(\hat{a}\) values above the threshold of 80. A **MIL-HDBK-1823A** analysis produced the results in Figure 1, with these results:

- a
_{50}= 59.8 - a
_{90}= 69.4 **a**_{90/95}**= 71.0**

These values are “correct” and are based on all 600 observations.

Now consider the situation where only 122 of the observations were “found.: (Note, too, that 122 would be considered a “large” sample size.) All that is known is what is contained in the 122 observations with \(\hat{a}\) above \(\hat{a}_{decision} = 80\), as illustrated in Figure 2.

*Figure 2 – OSL Regression Methods Over-estimate POD for Field-Finds*

The data in gray are unknown. Since only the observations above \(\hat{a}\) = 80 are known, they pull the left side of the line sharply upward because the data that would otherwise pull it back into place are not known. Furthermore, since a considerable fraction of the data is missing, the observed \(\hat{a} \textit{ vs a}\) scatter appears much smaller (probability densities in Figure 2) than it really is (Figure 1). The POD based only on the “finds” produces these results:

- a
_{50}= 43.7 - a
_{90}= 59.2 **a**_{90/95}**= 61.7**

That is, the *a _{90/95}* capability is optimistically and anti-conservatively too small by 9 size units (62 vs. 71). This is an enormous overestimation of POD capability. The POD (at 95% confidence) for

*a*= 62 is not 90% as a naive analysis would suggest, but about POD = 50%, as seen in Figure 1.

In other words we are 95% confident that the inspection will find about half the cracks of size 62 or larger, not 90% of them. This offers a partial explanation of why inspections whose capability is (erroneously) based on field-finds still experience large misses when inspecting future field-service parts.

There are several things to keep in mind: The data that are plotted in gray in Figure 2 are unknown to the analysis. They cannot be used, as in Figure 1, because they are known in Figure 1 but unknown in Figure 2. Furthermore, censored regression can’t work either because the cracks that were not found were *not known to have been missed*, which is a requirement for censored regression (see **MIL-HDBK-1823A**, Appendix G “Statistical Analysis of NDE Data”).

The sky is not falling, of course. Field-finds can be used as NDE specimens but they require special statistical methods to do that. I can help you and your enterprise analyzed field-finds correctly and avoid dangerous over-estimation of your system’s capabilities.