An integrated intelligence system for wear debris analysis

Peng, Zhongxiao (2002) An integrated intelligence system for wear debris analysis. Wear, 252 (9-10). pp. 730-743.

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Abstract

Wear debrisnext term generated from two moving surfaces inside a machine is a direct previous termwearnext term product of operating machinery. The study of the previous termdebrisnext term can reveal previous termwearnext term mechanisms, previous termwearnext term modes and previous termwearnext term phases undergoing in the machine. Hence, previous termwear debris analysisnext term can be a very useful means to assess the condition of the machine. However, the current techniques previous termfornext term individual particle previous termanalysisnext term are usually time-consuming and costly due to the requirement of analyst’s expertise to perform particle inspection, morphology characterisation and data interpretation. The limitation has obstructed the wide application of this method. Therefore, it is necessary to develop effective, reliable and cost-efficient techniques to perform previous termwear debris analysis fornext term industrial application. This paper presents a fully computerised package previous termfor wear debris analysis.next term The package includes three major previous termsystemsnext term corresponding to a three-dimensional particle previous termanalysis system,next term an automatic particle identification previous termsystemnext term and an expert previous termsystem,next term communicating with each other through user-friendly interfaces. The successful development of such a previous termsystemnext term has demonstrated the possibility to achieve a fully computerised previous termanalysis system fornext term routine and in-depth previous termwear debrisnext term study previous termfornext term machine condition monitoring and fault diagnosis.

Item ID: 658
Item Type: Article (Refereed Research - C1)
Keywords: automatic previous termwear debris analysisnext term, artificial previous termintelligencenext term techniques, machine condition monitoring
Additional Information:

© 2002 Elsevier. : This journal is available online - use hypertext links above.

ISSN: 1873-2577
Date Deposited: 23 Oct 2006
FoR Codes: 09 ENGINEERING > 0913 Mechanical Engineering > 091399 Mechanical Engineering not elsewhere classified @ 100%
SEO Codes: 86 MANUFACTURING > 8614 Machinery and Equipment > 861404 Mining Machinery and Equipment @ 60%
86 MANUFACTURING > 8614 Machinery and Equipment > 861401 Agricultural Machinery and Equipment @ 30%
86 MANUFACTURING > 8614 Machinery and Equipment > 861403 Industrial Machinery and Equipment @ 10%
Citation Count from Web of Science Web of Science 10
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