Guided Wave-Based Structural Health Monitoring of Rails: A Deep Learning Approach for Damage Detection
The structural integrity of railway rails is essential for the safety and efficiency of modern transportation networks, where early detection of damage is crucial to preventing catastrophic failures and service disruptions. Guided wave-based structural health monitoring (SHM) offers long-range and high-sensitivity inspection capabilities for rail infrastructure. However, the complex propagation characteristics of ultrasonic guided waves and the presence of noise in operational environments pose significant challenges for traditional signal processing methods. In this study, a deep learning-based framework is proposed for rail damage detection utilizing guided wave SHM. The methodology involves denoising, normalizing, and transforming the acquired ultrasonic signals into time–frequency representations, which, together with raw waveforms, are used as inputs to a long short-term memory (LSTM) network. The LSTM model is designed to automatically learn temporal dependencies and extract discriminative features for accurate damage identification. The proposed approach achieves superior detection accuracy compared to conventional techniques and maintains robustness under elevated noise conditions. These findings underscore the potential of integrating deep learning with guided wave SHM for intelligent and automated rail defect detection, paving the way for scalable monitoring solutions and enhanced railway infrastructure reliability.