A fractional Fourier transform-based method to detect impacts between the bogie and the car body of a railway vehicle: A data-driven approach

作者：Gutierrez Carvajal R E^*; Betancur German R; Castaneda Leonel F; Zajac G

来源：Proceedings of the Institution of Mechanical Engineers - Part F: Journal of Rail and Rapid Transit , 2018, 232(1): 288-296.

DOI：10.1177/0954409716675187

• Railway vehicle
• research and development
• superstructure
• vibration
• fractional Fourier transform
• vehicle structure monitoring
• machine learning

Structural railway transport elements are typically designed to work for at least 30 years without undergoing major maintenance. However, real-life operational conditions present behaviors different to the model predicted during the initial design phase, which affects the lifetime of the elements in question. This is the case of first-generation railway vehicles which operates in the city of Medellin, Colombia, as the bolster beam presented cracks after 12 years of operation, possibly due to undesired impacts between the bogie and the pivot of the bolster beam. Monitoring vibrational signals would give some sort of an insight into impact phenomena; however, herein lies the problem, as they are difficult to identify using only vibration signals, occurring during time events that take place in a speed-varying system. In this article, the authors present a technique that automatically detects impacts using multiple in-between time/frequency representations, ranking them according to their capacity to discriminate between impact events. Our results show that the best representation for this data was the Fractional Cepstrum Transform at order 0.5 (auROC=0.961), which outperformed the best pure domain descriptor by least 4%.

• 出版日期2018-1