Generalization method of hammering test by machine learning

Abstract

For the automation and mechanization of hammering inspection for improving the productivity of survey inspection, it is necessary to quantify the hammering evaluation. It is possible to classify hammering sounds into normal and defective parts with high accuracy by supervised learning using CNN (Convolutional Neural Network) of hammering sound data visualized in the time-frequency domain by short-time Fourier transform. However, the classification performance deteriorates when the characteristics of the members or the environment are different. In this paper, we propose a method to evaluate the feature vector of the test hammering sound of the test site from the feature vector of the normal hammering sound data of the test site by the Mahalanobis' distance, using the trained CNN only as a feature extractor. As a result, it was shown that the proposed method can quantitatively evaluate the soundness even at sites with different conditions by obtaining generalization performance.