An integrated intelligence system for wear debris analysis

Wear

Volumn 252, Issue 9-10, 2002, Pages 730-743

  Peng, Zhongxiao ^a  

^a JAMES COOK UNIVERSITY   (Australia)

Author keywords

Artificial intelligence techniques; Automatic wear debris analysis; Machine condition monitoring

Indexed keywords

DEBRIS; INDUSTRIAL APPLICATIONS; MACHINERY; MORPHOLOGY;

WEAR DEBRIS ANALYSIS;

WEAR OF MATERIALS;

ARTIFICIAL INTELLIGENCE; COMPUTER TECHNIQUE; MACHINE TOOL APPLICATION; VIBRATION MONITORING; WEAR DEBRIS;

EID: 0036557999     PISSN: 00431648     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0043-1648(02)00031-5     Document Type: Article

References (26)

1. Comparison of vibration and direct reading ferrographic techniques in application to high speed gears operating under steady-state conditions
2. Effective integration of vibration analysis and oil analysis
3. Wear particle atlas
4. Numerical characterisation of wear debris for machine condition monitoring
5. The determination of particle morphological parameters to assist in elucidation of the wear process
6. Automatic wear particle classification using neural networks
7. Wear particle analysis for machine condition monitoring using computer technologies
8. Application of lasernet fines to mechanical wear and hydraulic monitoring
9. Surface characterization of wear particles
10. Scanned-probe microscopes
11. Functional science of different techniques for surface morphology measurements
12. Characterization of surface topography of wear particles by SEM stereoscopy
13. Fiber-optic laser scanning confocal microscope suitable for fluorescence imaging
14. The development of three dimensional imaging techniques of wear particle analysis
15. A three-dimensional image analysis and classification system for wear debris
16. Machine condition monitoring using filtergram and ferrographic techniques
17. Numerical characterization of wear particle morphology and angularity of particles and surfaces
18. Computer image analysis of wear particles in three-dimensions for machine condition monitoring
19. Wear particle classification in fuzzy grey system
20. The development of an expert system for wear debris analysis