Wear particle classification in a fuzzy grey system

The analysis and identification of wear particles for machine condition monitoring is usually conducted by experienced inspectors, and, thus, the process is usually very time-consuming. To overcome this obstacle, grey system theory has been applied in this study. The theory of grey systems is a new technique to perform prediction, relational analysis and decision making in many areas. In this paper, the theory of grey relational grades has been used to classify six types of metallic wear debris whose three-dimensional images are acquired from laser scanning confocal microscopy. Their boundary morphology and surface topology are then described by certain numerical parameters. Since the parameters have different levels of significance for different types of wear debris for particle identification, weighting factors of the parameters have been taken into consideration. To determine the weighting factors for the study, fuzzy logic has been applied. This study has demonstrated that a grey system combined with fuzzy logic can be used to classify wear particles satisfactorily.

[1]  Thomas Brett Kirk,et al.  Two-dimensional fast Fourier transform and power spectrum for wear particle analysis , 1997 .

[2]  M. Dohnal,et al.  A fuzzy knowledge base of ball bearing wear and its practical applications , 1992 .

[3]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[4]  Pawel Podsiadlo,et al.  Characterization of surface topography of wear particles by SEM stereoscopy , 1997 .

[5]  Jiajun Liu,et al.  The generation of wear debris of different morphology in the running-in process of iron and steels , 1992 .

[6]  Vladimir J. Lumelsky,et al.  A combined algorithm for weighting the variables and clustering in the clustering problem , 1982, Pattern Recognit..

[7]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[8]  M. Harris,et al.  Fiber-optic laser scanning confocal microscope suitable for fluorescence imaging. , 1994, Applied optics.

[9]  J. Carroll,et al.  Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables , 1984 .

[10]  Cai Sijing,et al.  Grey system theory applied to rock mechanics , 1993 .

[11]  Thomas Brett Kirk,et al.  The development of three-dimensional imaging techniques of wear particle analysis , 1997 .

[12]  Bt Kuhnell,et al.  Diagnosis of Machine Fault Using Relational Grade Analysis in the Grey-system Theory , 1990 .

[13]  S. Paddock,et al.  Confocal laser scanning microscopy. , 1999, BioTechniques.

[14]  Anthony Peter Macmillan Coxon,et al.  The user's guide to multidimensional scaling : with special reference to the MDS (X) library of computer programs , 1983 .

[15]  James C. Bezdek,et al.  Nerf c-means: Non-Euclidean relational fuzzy clustering , 1994, Pattern Recognit..