An improved signal processing method for the laser displacement sensor in mechanical systems

Abstract

Abstract In recent years, the laser displacement sensor (LDS) has been widely applied to a multitude of fields such as precision measurement and reverse engineering. However, the accuracy of the LDS applied to these industries is severely restricted by signal processing methods including signal compensation and signal reconstruction. In view of this, a B-spline curve approximation is used to realize high-precise and efficient reconstruction of noisy signals of the LDS and an improved elitist clonal selection algorithm (ECSA) is proposed to address the multi-objective, continuous and nonlinear optimization problem in the B-spline curve approximation process, namely the knot adjustment problem. Additionally, an adaptive chaotic mutation operator is designed to improve the algorithm search efficiency, increase the population diversity and avoid the prematurity. And an antibody reselection strategy based on the antibody concentration and the antigen affinity vectorial moment is proposed. Subsequently, the Hannan-Quinn information criterion (HQIC) is used as an affinity measurement to judge and weigh the goodness of fit and computational complexity and to automatically and accurately calculate the number and location of internal knots, thereby reconstructing noisy signals with different features. Simulation results evidence that the improved algorithm can not only realize the automatic B-spline curve reconstruction of the noisy signals featuring continuity, discontinuity and sharp points but also surpass existing studies in the global convergence and convergence rate. And both the on-machine measurement experiment for API threads and the comparison experiments for measured thread parameters validate the excellent and powerful performance of the proposed method in processing LDS signals.