Inspection of corroded engineering components is vital for ensuring safety throughout the lifetime of infrastructure. However, full inspection can be infeasible due to time constraints, budgetary limits or restricted access. Subsequently there is growing interest in partial coverage inspection (PCI) techniques which use data from the inspection of a limited area to assess the condition of larger areas of a component. Extreme value analysis (EVA) is a tool for PCI, it allows an inspector to build a statistical model of the smallest thicknesses across a component. Construction of extreme value models relies on the selection of the smallest thicknesses from the inspection data. Current methodologies rely on the judgement of the analyst to select sets of thickness minima and frequently the inspection data is not checked to ensure that the assumptions made by EVA are reasonable. Consequently, the resulting models can be subjective and can provide inadequate models for extrapolation. In this paper, a framework for building extreme value models of inspection data is introduced. The method selects a sample of thickness minima such that the data is compatible with the assumptions of EVA. It is shown that this framework can select a suitable set of minima for a large number of correlated exponential and Gaussian surfaces and the method is tested using real inspection data collected from an ultrasonic thickness C-scan of a rough surface.