A Deep Learning-Based Surrogate Method for Cumulative Fatigue Damage Estimation of Heavy-Haul Locomotive Couplers

Abstract

The accumulation of fatigue damage in heavy-haul locomotive couplers significantly degrades their load-bearing capacity and accelerates structural failure, making accurate damage estimation imperative for operational safety. Although the rainflow counting method (RCM) has long served as the standard for quantifying cumulative fatigue damage under variable amplitude loading, existing RCM-based frameworks are hindered by computationally exhaustive load history traversals and the cumbersome process of repeatedly mapping massive load cycles to physical priors. To address these issues, a dual-order synergistic complementary attention network (DSCANet) surrogate model for cumulative fatigue damage estimation of couplers is proposed in this article. First, operating on meticulously extracted multidomain inputs, an encoder-decoder architecture with skip connections is employed to perform high-dimensional mapping and reconstruction of inputs, effectively preserving critical damage-inducing features. To comprehensively capture fatigue degradation characteristics, the model divides damage assessment into two complementary subtasks. Specifically, the local branch mines cross-domain coupling relationships among damage-contributing features through multiscale dilated convolutions, while the global branch models damage accumulation dependencies and dynamically weights feature importance via a dual-path complementary attention mechanism with asymmetric head designs. The adaptive fusion of these two representations enables precise evaluation of cumulative fatigue damage. Experimental results demonstrate that the proposed DSCANet accurately predicts cumulative fatigue damage while outperforming traditional methods in computational efficiency.