Fatigue Monitoring Signals with Remaining Life in Railway Axle Components: Optimization Modeling

Abstract

The reliable operation of high-speed railway systems is fundamentally dependent on the structural integrity of critical components, particularly railway axles. Continuous mechanical stress and environmental factors inevitably lead to material degradation, necessitating advanced fatigue monitoring and remaining useful life prediction strategies. This paper proposes a comprehensive optimization modeling approach for analyzing fatigue monitoring signals and accurately estimating the remaining useful life of railway axle components. By integrating multidimensional sensor data, advanced signal processing techniques, and heuristic optimization frameworks, the proposed methodology addresses the complexities inherent in non-stationary, noise-corrupted field data. The research systematically investigates the theoretical underpinnings of fatigue crack initiation and propagation, translating these physical phenomena into data-driven predictive models. Feature extraction mechanisms are designed to capture both time-domain and frequency-domain characteristics, which are subsequently refined through a robust feature selection algorithm to eliminate redundancy. Furthermore, an optimization framework is introduced to fine-tune the hyperparameters of the predictive model, significantly enhancing its generalization capability across diverse operational conditions. Experimental validation utilizing extensive field data demonstrates the superiority of the proposed optimized model over conventional predictive techniques in terms of accuracy and computational efficiency. This study contributes to the broader field of structural health monitoring by providing a scalable, reliable framework for predictive maintenance in railway engineering.