Abstract
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.
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