On-board detection of rail corrugation using improved convolutional block attention mechanism

Abstract

Leveraging acceleration sensors affixed to the train body enables continuous surveillance of rail corrugation, delivering cost-effectiveness, operational efficiency, and portability. Establishing the correlation between vertical body acceleration and rail corrugation poses a substantial challenge. To ensure uninterrupted monitoring of rail corrugation, an initial development involved constructing a train-track integrated simulation model that accounted for the dynamics of flexible wheelsets and tracks, thereby generating a simulated dataset of vertical body acceleration. Subsequent improvements were made to the conventional Convolutional Block Attention Module (CBAM) architecture, culminating in the proposal of a deep one-dimensional convolutional residual network model named Train Body Vertical Acceleration Network (TBVA-Net), founded on an improved CBAM framework. Training was conducted using the simulated dataset, showcasing the reduced model complexity and total parameter count of the improved CBAM architecture, which notably amplified classification accuracy. The TBVA-Net, employing the refined CBAM, consistently achieved test accuracies exceeding 95%, averaging at 98.6% on the simulated dataset. Validation through field-measured data corroborated the rationale behind the proposed TBVA-Net architecture. Fine-tuning with a limited subset of labeled field data led to a transfer accuracy of 98.5%. This paper presents an innovative approach for detecting rail corrugation through vertical acceleration signals obtained from operational vehicles.