Carriage interior noise-based inspection for rail corrugation on high-speed railway track. (July 2022)

• Record Type: Journal Article
• Authors: Wei, Zilong; Sun, Xianfu; Yang, Fei; Ke, Zaitian; Lu, Tao; Zhang, Pan; Shen, Chen
• Is Part Of: Applied acoustics. Volume 196(2022)
• Publication Date: 2022-07
• Subjects: Carriage interior noise -- Rail corrugation -- High-speed railway -- Acoustic inspection; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2022.108881
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: An on-board instrumentation capable of inspecting rail corrugation is proposed. Carriage interior noise is employed to distinguish rail corrugation on HSR tracks. The method accounts for different train speeds and track radii on railway lines. The method succeeds in identifying position, wavelength and severity of corrugation. Abstract: The presence of rail corrugation enlarges the wheel-rail impact and exacerbates the failure of track components, and the situation becomes even worse under high train speed, which promotes the urgent need for an efficient and easily accessible inspection method. Conventional diagnosis approaches such as axle box acceleration (ABA) and image recognition measurements, however, require complex instrumentations on the running gear, restricting their applications on a wide range of operational trains. In this study, we investigate the capability of carriage interior noise in diagnosing rail corrugation on the high-speed railway (HSR). For this purpose, train-borne vibration & noise measurements were integrated with in-situ rail surface irregularity tests, to extract the characteristic carriage interior responses induced by rail corrugation. The measurements were conducted on two HSR tracks with different corrugation geometries, and the time–frequency distributions of interior noise were identified under different train speeds and with different track radii. Afterward, an interior noise-based inspection algorithm was proposed by proper correlation of the gained data, and was then demonstrated on a third HSR track with an unknown rail surface condition. The comparison between the proposed inspection algorithm and the widely-recognized ABA measurements indicates that the interior noise succeeded in identifying the position, typical wavelength and severity of rail corrugation under varying train speeds. The work advances a cost-effective and easily accessible way for the condition monitoring of railway tracks.