Damage identification for railway tracks using ultrasound guided wave and hybrid probabilistic deep learning

Abstract

Rails are often damaged by train extrusion and other external forces, which seriously affects track safety. Although deep learning is widely used in rail damage recognition, its usual inability to understand own uncertainty that is critical in engineering application. Hence, this paper proposes a hybrid probabilistic deep learning for rail damage identification. It combines the hierarchical representation capabilities of deep learning with probability distributions. The posterior distribution provides support for the uncertainty quantification in identification results. Firstly, ultrasonic guided waves were used to sense the rail damage and time-frequency feature map dataset was obtained by analysing its time-domain signal using wavelet transform. Then, two different weight perturbation methods are used for comparative study. The reparameterization method performs more consistently and efficiently in this task, with a recognition accuracy of 0.9400. Subsequently, the effect of the number of probabilistic and non-probabilistic layers in the hybrid network on recognition results was analysed. Experimental results showed that hybrid probabilistic deep learning achieved the highest testing accuracy of 0.9900. The uncertainty quantification metrics of recognition results from hybrid probabilistic deep learning are mostly less than 0.2, demonstrating favourable reliability.