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Using fuzzy c-means and fuzzy integrals for machinery fault diagnosis

Liu, Xiaofeng, Ma, Lin, Zhang, Sheng, & Mathew, Joseph (2005) Using fuzzy c-means and fuzzy integrals for machinery fault diagnosis. In International Conference on Condition Monitoring, 18-21 July 2005, Cambridge, England.

Abstract

This research applied fuzzy c-means and fuzzy integral theories to a proposed novel two-step machinery fault diagnosis model. Distributed multiple fuzzy c-means classifiers were used to produce an initial diagnosis result by considering different features. Fuzzy measure and fuzzy integral data fusion theory was then applied to combine the initial diagnosis results into a consensus final decision. Vibration signals from rolling element bearings were used to validate the method. Results showed that the proposed approach using fuzzy c-means and fuzzy integral techniques improved the diagnosis accuracy and reduced the computation load.

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673 since deposited on 14 Apr 2008
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ID Code: 13300
Item Type: Conference Paper
Keywords: Sugeno fuzzy integral, fuzzy c, means, data fusion, 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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