Soft computing approach to fault diagnosis of centrifugal pump

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@Article{Sakthivel20121574,
  author =       "N. R. Sakthivel and Binoy. B. Nair and V. Sugumaran",
  title =        "Soft computing approach to fault diagnosis of
                 centrifugal pump",
  journal =      "Applied Soft Computing",
  volume =       "12",
  number =       "5",
  pages =        "1574--1581",
  year =         "2012",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2011.12.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494611004935",
  keywords =     "genetic algorithms, genetic programming, Centrifugal
                 pump, Gene expression programming, Fault diagnosis,
                 Statistical features, Decision tree algorithm, Support
                 vector machine, Proximal support vector machine",
  abstract =     "Fault detection and isolation in rotating machinery is
                 very important from an industrial viewpoint as it can
                 help in maintenance activities and significantly reduce
                 the down-time of the machine, resulting in major cost
                 savings. Traditional methods have been found to be not
                 very accurate. Soft computing based methods are now
                 being increasingly employed for the purpose. The
                 proposed method is based on a genetic programming
                 technique which is known as gene expression programming
                 (GEP). GEP is somewhat a new member of the genetic
                 programming family. The main objective of this paper is
                 to compare the classification accuracy of the proposed
                 evolutionary computing based method with other pattern
                 classification approaches such as support vector
                 machine (SVM), Wavelet-GEP, and proximal support vector
                 machine (PSVM). For this purpose, six states viz.,
                 normal, bearing fault, impeller fault, seal fault,
                 impeller and bearing fault together, cavitation are
                 simulated on centrifugal pump. Decision tree algorithm
                 is used to select the features. The results obtained
                 using GEP is compared with the performance of
                 Wavelet-GEP, support vector machine (SVM) and proximal
                 support vector machine (PSVM) based classifiers. It is
                 observed that both GEP and SVM equally outperform the
                 other two classifiers (PSVM and Wavelet-GEP) considered
                 in the present study.",
}

Genetic Programming entries for N R Sakthivel Binoy B Nair V Sugumaran

Citations