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A gear inspection common-region comparison evaluates repeated surface observations on the same registered portion of a tooth flank. It prevents a changed gear pitting inspection mask from being mistaken for a change in damage. The region must be common in surface coordinates, not merely occupy the same pixel positions in two camera images.

After image registration, let $m_i^{(0)}$ and $m_i^{(1)}$ be inspection-mask indicator functions on one reference grid. Their common mask isIf $p_i^{(t)}$ classifies pitting at time $t$ and $a_i$ is represented surface area, defineThe identical denominator makes the difference a comparison within the same eligible area. It does not eliminate errors in registration or image segmentation.

An original example has 10000 eligible equal-area pixels at the first inspection and 8000 at the second, with 7000 in common. In the common region, pitted counts rise from 140 to 210: fractions rise from $2\%$ to $3\%$, a change of one percentage point or $50\%$ relative to the original fraction. Suppose total pitted counts were 200 and 240 across the separate full masks. Their raw difference, 40 pixels, is not the same quantity as the 70-pixel increase within the common region.

A gear inspection coverage audit should also record the excluded areas and why they disappeared or became newly visible. An intersection-only result can miss damage on an edge that the second camera view no longer captures. Keeping the original images makes that loss of coverage visible to a later reviewer.

Common inputs can correlate the two estimated fractions. In general,This is the usual correlated-input uncertainty propagation, not an assumption that neighboring image pixels are independent.^[ref] A primary NASA endurance study linked repeated physical inspections with vibration and debris observations; the common-region formalism specifies one way to make such repeated surface measurements comparable.^[ref]

A gear inspection missing-area bound limits the whole-target pitting fraction using the area actually observed and explicit constraints on the unseen area. It formalizes what a gear inspection coverage audit can establish without assuming that excluded regions resemble visible ones.

Let $c=A_{\mathrm{seen}}/A_{\mathrm{target}}$, let $p$ be the observed gear pitting area fraction, and let $u$ be the unseen fraction affected by pitting. For a consistent surface-area convention,With only $0\le u\le 1$ known,The interval width is exactly $1-c$. Improving classification precision within the visible area cannot by itself remove uncertainty from a completely unseen region.

For an original example with $c=0.9$ and $p=0.02$, the possible whole-target fraction ranges from $0.018$ to $0.118$. The observed $2\%$ therefore does not prove that the whole surface is below an illustrative $5\%$ criterion. That chosen criterion is not a gear safety or acceptance standard.

The worst-case upper bound meets a target $\theta <1$ only iffor $p<1$. At $p=0.02$ and $\theta =0.05$, at least about $96.94\%$ coverage is required for this particular worst-case guarantee.

Additional justified information can narrow the range. If a separate observation establishes $0\le u\le 0.1$, then $cp\le \Phi \le cp+0.1(1-c)$. At $c=0.8$ and $p=0.02$, this gives $0.016\le \Phi \le 0.036$. The tighter bound depends on the new evidence about the unseen region, not on a mathematical preference for a smaller interval.

These deterministic bounds assume the observed classification and represented areas are correct. Image registration, image segmentation and geometric uncertainties can require additional allowances. They are not confidence intervals and do not describe pit depth or remaining fatigue life. The primary NASA endurance study combined inspection with vibration and debris monitoring; these different observations constrain different aspects of damage.^[ref]