Intelligent Flaw Detection in Eddy Current Inspection Data Through Machine Learning Model

The paper presents a robust machine learning model for automated classification of flaw signals from eddy current inspection data of heat exchanger tubes. The proposed model employs four sliding window based ingenious features namely variance, template correlation, template dynamic time warping distance and area under the signal with Random Forest supervised machine learning model, to identify flaws. The efficacy of the model is evaluated on tube inspection data acquired in a heat exchanger by comparing its performance against expert analysis. The machine learning model exhibits an impressive accuracy of 99.94% for classification of flaw signals in addition to higher desirable metrics such as precision, recall, F1-score and Matthews correlation coefficient (MCC). This work lays a strong foundation for developing a real-time, robust and reliable flaw detection system.