Application of an information fusion method to compound fault diagnosis of rotating machinery

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Hu:2015:CCDC,
  author =       "Qin Hu and Aisong Qin and Qinghua Zhang and 
                 Guoxi Sun and Longqiu Shao",
  booktitle =    "The 27th Chinese Control and Decision Conference (2015
                 CCDC)",
  title =        "Application of an information fusion method to
                 compound fault diagnosis of rotating machinery",
  year =         "2015",
  pages =        "3859--3864",
  abstract =     "Aiming at how to use the multiple fault features
                 information synthetically to improve accuracy of
                 compound fault diagnosis, an information fusion method
                 based on the weighted evidence theory was proposed to
                 effectively diagnose compound faults of rotating
                 machinery. Firstly multiple fault features were
                 extracted by the genetic programming. Each fault
                 feature was separately used to act as evidence and the
                 initial diagnosis accuracy was regarded as the weight
                 coefficient of the evidence. Then through the negative
                 selection algorithm, the diagnosis ability of the local
                 diagnosis was advanced and an impersonal means of
                 obtaining basic probability assignment was given.
                 Finally the fusion result was obtained by using the
                 weighted evidence theory into the decision-making
                 information fusion for the preliminary result. By
                 comparing the diagnosis results with other artificial
                 intelligence algorithm, experiment result indicates
                 that using multiple weighted evidences fusion can
                 improve the diagnostic accuracy of compound fault.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CCDC.2015.7162598",
  ISSN =         "1948-9439",
  month =        may,
  notes =        "Also known as \cite{7162598}",
}

Genetic Programming entries for Qin Hu Aisong Qin Qinghua Zhang Guoxi Sun Longqiu Shao

Citations