Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27829
Title: Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Authors: Xu, BG 
Keywords: Bayesian belief network
Fault diagnosis
Flexible rotor
Uncertainty inference
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 1, p. 816-822 How to cite?
Journal: Expert systems with applications 
Abstract: Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts' knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.
URI: http://hdl.handle.net/10397/27829
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2011.07.079
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