Vibroacoustic Methods for Wheel-Flat Detection: Toward Safer and More Energy-Efficient Rail Transport—A Review

Flat spots on railway wheels critically threaten operational safety, accelerating track damage, noise pollution, and energy waste. Their repetitive, high-magnitude impacts dissipate mechanical energy as ground vibration and noise, directly reducing traction efficiency. This paper presents a comprehensive review of recent vibration and acoustic detection methods, comparing onboard and stationary wayside systems. The literature from 2019 to 2025 shows a trend toward machine learning and deep learning, often reaching nearly 100% accuracy in laboratory or simulated settings. However, most studies lack real-world validation with naturally occurring defects. Bridging this gap requires industry–academic collaboration for operational data and testing. Crucially, systems must classify defect severity in line with maintenance thresholds rather than focus on minor, non-actionable faults. Integrating these technologies into condition-based maintenance and predictive digital twins will enable optimization of scheduling and work orders. Future efforts should leverage edge computing for real-time analysis, federated learning for data scarcity, and energy harvesting for sensor autonomy. The goal is to develop field-validated, integrated systems that enhance safety, reduce energy loss, and improve reliability.