Impact load identification of high-speed train scale carbody based on multi-objective optimisation and TwIST sparse regularisation

Abstract

ABSTRACT Effective identification of dynamic loads on vehicle systems is crucial for evaluating the structural integrity of railway vehicles. The inverse problem method has been proposed for dynamic load identification to determine the impact load on the carbody. Due to the sparse nature of the impact load, the combination of the l 1-norm and TwIST (two-step iterative threshold shrinkage method) methods is used to reconstruct the impact load, taking into account the influence of sensor placement on the identification results. The MOHBA (Multi-objective Optimisation Honey Badger Algorithm) was proposed to simultaneously select the regularisation and iteration parameters. This algorithm aims to improve the accuracy of subsequence load reconstruction by considering the influence of noise levels when selecting the measured data. The impact load experiment was conducted on the scale carbody, and the single-point impact load and multi-point impact load matched well with the actual load. Satisfactory load identification results can still be achieved when the noise level is within 15%. When arranging measurement points on the boundary of the vehicle body, satisfactory load identification results can also be achieved.