Fractal-wavelet based classification of tribological surfaces

Classification of the topography of freshly machined, worn and damaged surfaces (e.g. damaged by adhesion, scoring, abrasion, pitting) is still a problem in machine failure analysis. Tribological surfaces often exhibit both a multiscale nature (i.e. different length scales of surface features) and a non-stationary nature (i.e. features which are superimposed on each other and located at different positions on a surface). The most widely used approaches to surface classification are based on the Fourier transform or statistical functions and parameters. Often these approaches are inadequate and provide incorrect classification of the tribological surfaces. The main reason is that these techniques fail to simultaneously capture the multiscale nature and the non-stationary nature of the surface data. A new method, called a hybrid fractal-wavelet method, has recently been developed for the characterization of tribological surfaces in a multiscale and non-stationary manner. In contrast to other methods, this method combines both the wavelets’ inherent ability to characterize surfaces at each individual scale and the fractals’ inherent ability to characterize surfaces in a scale-invariant manner. The application of this method to the classification of artificially generated fractal and tribological surfaces (e.g. worn surfaces) is presented in this paper. The newly developed method has been further modified to better suit tribological surface data, including a new measure of differences between initial and decoded images. The accuracy of this method in the classification of surfaces was assessed.