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.　©　2025　Elsevier　Ltd