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Machinery fault diagnosis schemas based on fuzzy integral theory

Liu, Xiaofeng, Ma, Lin, Zhang, Sheng, & Mathew, Joseph (2005) Machinery fault diagnosis schemas based on fuzzy integral theory. In International Conference on Condition Monitoring, 18-21 July 2005, Cambridge, England.

Abstract

Fuzzy measure and fuzzy integral theory is an outgrowth of classical measure theory and has found applications in image processing and information fusion. This paper presents the review of a study on the development of a machinery fault diagnosis application of fuzzy measures and fuzzy integrals. Fuzzy measures and fuzzy integrals take into account the index of importance of criteria and interactions among them--an important feature that makes fuzzy measure and fuzzy integral a good candidate for application in machinery fault diagnosis. The theory of fuzzy measures and fuzzy integrals and their important properties are introduced. Techniques for identifying fuzzy measures are summarised. Two schemas using fuzzy measures and fuzzy integrals for machinery fault diagnosis are also proposed.

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ID Code: 13299
Item Type: Conference Paper
Keywords: Data fusion, Fuzzy measures, Fuzzy integrals, Fault diagnosis
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2005 (please consult author)
Deposited On: 14 Apr 2008
Last Modified: 29 Feb 2012 23:12

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