Influence Factors of Fractal Characterization of Reciprocating Sliding Wear Surfaces

The principal purpose of this paper is to investigate influence factors of fractal characterization of reciprocating sliding wear surfaces. The wear testing was completed to simulate the real running condition of the diesel engine 8NVD48A-2U. The test results of wear surface morphology dimension characterization show that wear surface profiles have statistical self-affine fractal characteristics. In general, there are no effects of the profilometer sampling spacing and sampling length and evaluation length on the fractal dimensions of the surfaces. However, if the evaluation length is too short, the structure function logarithm of the surface profile is scattered. The sampling length acting as a filter is an important part of the fractal dimension measurement. If the sampling length is too short, the evaluation of the fractal dimension will have a larger standard deviation. The continuous wavelet transform can be used to improve surface profile dimension characterization.