Multi-agent decision fusion for motor fault diagnosis

Niu, Gang, Han, Tian, Yang, Bo-Suk, & Tan, Andy C. C. (2007) Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing, 21(3), pp. 1285-1299.

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Improvement of recognition rate is ultimate aim for fault diagnosis researchers using pattern recognition techniques. However, the unique recognition method can only recognise a limited classification capability which is insufficient for real-life application. An ongoing strategy is the decision fusion techniques. In order to avoid the shortage of single information source coupled with unique decision method, a new approach is required to obtain better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources from different types of sensors and decisions of multiple classifiers. First, non-commensurate sensor data sets are combined using an improved sensor fusion method at a decision level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent classifiers fusion algorithm is employed as the core of the whole fault diagnosis system. The efficiency of the proposed system was demonstrated through fault diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best individual classifier with single-source data.

Impact and interest:

80 citations in Scopus
49 citations in Web of Science®
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ID Code: 14741
Item Type: Journal Article
Refereed: Yes
Keywords: Fault diagnosis, Multiple, sensors data fusion, Correlation measure, Multi, agent algorithm, Classifiers fusion
DOI: 10.1016/j.ymssp.2006.03.003
ISSN: 0888-3270
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Mechanical Engineering not elsewhere classified (091399)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2007 Elsevier
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 05 Sep 2008 00:00
Last Modified: 29 Feb 2012 13:40

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