Machinery Fault Diagnosis Based on Feature Level Fuzzy Integral Data Fusion Techniques

Xiaofeng, Liu, Ma, Lin, & Mathew, Joseph (2006) Machinery Fault Diagnosis Based on Feature Level Fuzzy Integral Data Fusion Techniques. In IEEE International Conference on Industrial Informatics, 2006, Aug 2006, Singapore.

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Fuzzy methods for machinery fault diagnosis are able to classify fault patterns in a non-dichotomous way thereby imitating the way humans process vague information. As an outgrowth of classical set and measure theory, fuzzy measure and fuzzy integral theory has the ability to infer the importance of each criterion and represent certain interactions among them. Based on fuzzy measure and fuzzy integral theory, a novel feature level direct fuzzy data fusion approach for machinery fault diagnosis is presented. Fuzzy analysis method was used to obtain the membership values of each feature for each fault class. The Choquet fuzzy integral data fusion method was employed to produce the diagnostic result using different features. Current and vibration signals from electrical motors were used to validate the method. Results showed that the proposed feature level fuzzy measure and fuzzy integral fusion approach performed very well for electrical motor fault diagnosis.

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ID Code: 9458
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/INDIN.2006.275689
ISBN: 0780397002
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 12 Sep 2007 00:00
Last Modified: 11 Oct 2012 01:06

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