Morphological analysis of wear debris: Current state of available solutions

Seija Hietanen

Research output: Book/ReportReport

Abstract

For a computer to automatically identify wear and contaminant particles it is essential to develop image processing software robust enough to extract the shape and surface features of the particle accurately and independently of the quality and content of the image. The texture, colour and shape of the particle reveal the wear mode that generated the particle in a machine. Usually what we see on the computer screen is the result of an attack while the particle is in the lubricant, and distortions generated by the microscope and vision system used to produce the image. Wear particles and surfaces are three-dimensional objects and their numerical characterisation and classification is still largely an unresolved problem. Usually a set of various parameters is used to describe the surface topography. These parameters are, however, of limited use, especially when dealing with anisotropic surfaces. In this report the main emphasis is on the current state of optical, microscope and camera based image analysis techniques and different approaches used with them to analyse wear debris morphology. LaserNet Fines combines the standard oil analysis techniques of particle counting and shape classification into a single analytical instrument, by combining imaging technology and neural net shape classification. LaserNet is an optically based debris monitoring technology that determines the existence, type, severity and rate of progression of mechanical faults by measuring the size distribution, shape characteristics and rate of production of debris particles. Other methods used are computer aided vision engineering (CAVE); a program developed to analyse particle morphology quantitatively, a software-based wear debris classification system - SYCLOPS (systematic classification of oil-wetted particles), Jetscan; a SEM and EDX system in which the process of particle identification, measurement, characterisation and diagnosis is automatic, and EDAX SEM - EDS method.
Original languageEnglish
Place of PublicationEspoo
PublisherVTT Technical Research Centre of Finland
Number of pages13
Publication statusPublished - 2002
MoE publication typeD4 Published development or research report or study

Publication series

SeriesVTT Manufacturing Technology. Research Report
NumberBVAL 021231

Keywords

  • wear debris analysis
  • morphological analysis

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