Study on the Application of Improved Morphological Component Analysis Method in the Analysis of Vehicle Half-Axle Load

The measured torque signal of the vehicle half-axle is a periodic signal with rotational frequency and harmonics as the main components and interspersed with burr interference. Therefore, it is necessary to remove burrs from the measured signals for noise reduction before transforming them into bench load spectra based on the measured road signals. In this paper, we propose a method based on the improved morphological component and apply it to the burr removal of the measured signals of rotating components to achieve the separation of the actual half-axis load signals from the burr signals. The morphological component analysis based on fast adaptive step iterative shrinkage and P-index threshold noise reduction is proposed to address the drawback of slow convergence and poor noise reduction of the MCA algorithm based on iterative soft threshold shrinkage method. The simulation and the calculation results of a pure electric vehicle half-axle measured torque signal show that the improved morphological component analysis is significantly better than the traditional morphological component analysis in terms of convergence speed, and can effectively separate the burr components in the half-axle load signal.