Online estimation of fatigue damage of railway bogie frame based on axle box accelerations

Abstract

With continuously increasing operational mileages, the residual lifetime of rail vehicles has become a major concern in vehicle maintenance. This paper thus aims to develop an online estimation of fatigue damage to bogie frames, based on axle box accelerations, so as to elaborate the degradation of structural fatigue. In this study, the 3-axis accelerations of the axle box and dynamic stresses of the concerned hotspot in the bogie frame are taken as inputs and outputs, to determine the transfer function between the acceleration and stress. This permits us to calculate the power spectra density (PSD) of dynamic stress of concerned points in the frequency domain, based on the axle box acceleration. Subsequently, the frequency-domain fatigue life estimation method can be employed to determine real-time damage, based on the obtained stress PSDs. Finally, a metro bogie is used to validate the proposed method. The results suggest that the proposed method could serve as an effective solution for the structural health monitoring of rail vehicles, by characterising the degradation of residual service lifetime, and further facilitate the development of condition-based maintenance or damage-based maintenance.

KEYWORDS:

• Bogie frame
• antenna beam
• axle box acceleration
• transfer function
• stress power spectra density
• online estimation of fatigue damage

Acknowledgements

The present work has been supported by the National Science Foundation for Young Scientists of China (No. 51805450), China Association of Science and Technology Young Talent Support Project (No. 2019QNRC001), Sichuan Science and Technology Program (No. 2020YJ0075 and No. 2020JDTD0012), the Science and Technology Research Plan of China Railway General Corporation (No. P2019J002) and the National Key Research and Development Program of China (No. 2018YFE0201401-01).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The present work has been supported by the National Science Foundation for Young Scientists of China [No. 51805450], China Association of Science and Technology Young Talent Support Project [No. 2019QNRC001], Sichuan Science and Technology Program [No. 2020YJ0075 and No. 2020JDTD0012], the Science and Technology Research Plan of China Railway General Corporation [No. P2019J002] and the National Key Research and Development Program of China [No. 2018YFE0201401-01].