Abstract
Early detection of Rail Surface Spot Irregularities (RSSI), when damage is minor in severity, holds significant advantages for maintenance operations. Minor RSSI can be simply resurfaced, which is far more cost-effective compared to the rail replacement often necessitated by advanced RSSI. This study presents a novel RSSI detection method which consists of a hybrid approach combining Wavelet Packet Analysis (WPA) and the Hilbert-Huang Transform (HHT). This proposed technique capitalizes on the strengths of each method, mitigating their individual limitations and resulting in improved overall performance. The method extracts RSSI locations along a track through analysis of Axle Box Acceleration (ABA) data from an in-service train. The presented algorithm is implemented in a numerically simulated environment that accounts for various levels of background noise contamination in the measurements. The hybrid method successfully identified the locations and dimensions of the localized imperfection on each the surface.
