DYNAMIC IDENTIFICATION OF WEAR STATE BASED ON NONLINEAR PARAMETERS

Abstract

This paper presents a new methodology of wear state recognition by using fractal parameters, multifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonlinear state point of worn surface is established by fractal dimension, average diagonal length and spectrum width. Further, a steady state sphere is obtained by the nonlinear state point and [Formula: see text]-means clustering algorithm. Results show that fractal, multifractal and recurrence parameters characterize the worn surface from different perspectives. They should be used simultaneously to comprehensively characterize the integral structures, partial structures and internal structures of worn surface. The proposed nonlinear state point shows a variation process of concentration–stabilization–separation during the wear process. The wear states can be identified effectively by the relationship between nonlinear state points and steady state sphere.