A Statistical Method for Crack Detection from Vibrothermography Inspection Data

Nondestructive evaluation methods are widely used in quality control processes for critical components in systems such as aircraft engines and nuclear power plants. These same methods are also used in periodic field inspection to assure that the system continues to be reliable while it is in service. This paper describes a detection algorithm to automatically analyze vibrothermography sequence-of-image inspection data used to detect cracks in jet engine fan blades. Principal components analysis is used for dimension reduction. Then the fitted coefficients of the first principal component are processed by using robust regression to produce studentized residuals that are used in the crack-detection rules. We also show how to quantify the probability of detection for the algorithm. The detection algorithm is both computationally efficient and accurate. It correctly identified several cracks in our experimental data that were not detected by the standard human visual inspection method.