Methods for indication classification in thermal acoustic imaging inspection

Abstract

Methods are used by a thermal acoustic imaging (TAI) inspection system to identify potential defects within a component scanned, such as an engine fan blade. The inspection system generates a TAI scan having a plurality of frames. Indications are identified in at least one frame of the plurality of frames. An extractor model is applied to the plurality of frames to extract a plurality of spatial features corresponding to the indication. The plurality of spatial features is concatenated from each frame to generate a time series value of the values for the features. The plurality of spatial features is combined into multiple sequence data. The multiple sequence data includes a plurality of temporal features. The multiple sequence data is provided to train a neural network model to predict defects. For trained neural network models, the multiple sequence data is used to predict if the indication is a defect.