The parameter identification of metro rail corrugation based on effective signal extraction and inertial reference method

Rail corrugation is a severe track damage and failure problem in metro lines. So, the accurate assessment of rail corrugation is significant for the safety and comfort of vehicles. It is an efficient means to detect rail corrugation through the acceleration signal. However, the acceleration signal contains the mixed signal caused by vehicle component noise and wheel polygonal wear, resulting in poor rail corrugation detection accuracy. Therefore, a new method is presented to extract the effective signal components and accurately identify the rail corrugation. Firstly, the Cubic mapping, modified sine algorithm, and Le ' vy flight are introduced into the dung beetle optimizer algorithm. A new improved dung beetle optimizer (IDBO) algorithm is presented to optimize the hyperparameters of the VMD algorithm, which can increase the signal decomposition performance of the VMD. Secondly, the fast independent component analysis method separates the independent components (ICs) from the VMD modes and original acceleration signal. Based on the selection criteria of ICs, the dominant signal components related to rail corrugation are determined. The rail corrugation amplitude can be obtained through the inertial reference method. The experimental results demonstrate that the presented method has better rail corrugation recognition accuracy, the root mean square error is smaller than the comparison method, and the corrugation wavelength of the straight track is larger than the curve track.