Improvement of impact acoustic inspection for high-speed railway track slabs using time–frequency analysis and non-defective machine learning

Abstract

Slab track is one of the most highly used ballastless track forms in Japan's high-speed railway system (Shinkansen) and is composed from track slabs (precast RC or PRC member), a filling layer (mixture of cement, asphalt emulsion and fine aggregates) and concrete bed. As they age, it has been reported that the supporting conditions of track slabs change due to damage in the filling layer that in turn affects running stability. It is necessary to evaluate supporting conditions of track slabs accurately during maintenance of slab tracks prior to signs of cracking. Some previous works show the usefulness of impact acoustics and non-defective machine learning using frequency response spectrums as feature values for evaluating supporting conditions of track slabs. In this study, we examine a method of non-defective learning to improve the evaluation of supporting conditions: time-frequency response characteristics of impact acoustics using time-frequency analysis and resulting spectrograms as feature values. As a result, we have improved accuracy rate (from 81.90 % to 86.72 %) and F-value (from 70.79 % to 78.75 %) more precisely than conventional method based on frequency response functions as feature values.