Study of rail damage diagnosis and localization method based on intelligent wireless acoustic sensor network

Abstract

Many major derailments are closely related to rail damage, so it is especially important to conduct regular damage detection on in-service rails. In this paper, with the main objective of nondestructive detection and localization of rail crack damage, a wireless acoustic sensor network (WASN) based rail flaw detection and localization diagnosis system is designed and implemented: firstly, the WASN nodes rapidly sample the acoustic emission signal on the rail surface, then calculate and wirelessly transmit its characteristic parameters to the gateway, and then use the proposed information wasserstein generative adversarial network-gradient penalty (InfoWGAN-GP) algorithm for data augmentation of the collected AE features, followed by feature optimization of the augmented hybrid data set based on the proposed categorical boosting-borutashap (Catboost-BorutaShap) algorithm, furthermore, the proposed improved aquila optimizer-deep extreme learning machine (IAO-DELM) and binary dwarf mongoose simulated annealing optimization-newton iteration-triangulation algorithm (BDMSAO-NI-TA) strategies are then proposed to establish accurate identification and localization of rail crack damage derivation models. The experimental comparison results show that the IAO-DELM model constructed based on the preferred feature subset has the best effect on the performance enhancement of rail crack damage diagnosis, with the accuracy, precision, F1-score, weighted average and AUC of 99.86%, 99.58%, 98.75%, 0.9962 and 0.9923, respectively, which are higher than those of the optimization algorithm such as honey badger combined with DELM model. The evaluation indexes indicate that this method has excellent monitoring effectiveness.