Bearing Fault Diagnostics Based on Reconstructed Features

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

@InProceedings{Liu:2008:ieeeIAS,
  author =       "J. Liu and S. Ghafari and W. Wang and 
                 F. Golnaraghi and F. Ismail",
  title =        "Bearing Fault Diagnostics Based on Reconstructed
                 Features",
  booktitle =    "IEEE Industry Applications Society Annual Meeting, IAS
                 '08",
  year =         "2008",
  month =        oct,
  pages =        "1--7",
  keywords =     "genetic algorithms, genetic programming, bearing
                 condition monitoring, bearing fault diagnostic
                 technique, fault diagnostic reliability, feature
                 reconstruction, modified kurtosis ratio, one-scale
                 wavelet analysis, condition monitoring, fault
                 diagnosis, feature extraction, image reconstruction,
                 machine bearings, wavelet transforms",
  DOI =          "doi:10.1109/08IAS.2008.173",
  ISSN =         "0197-2618",
  abstract =     "Rolling-element bearings are widely used in various
                 mechanical and electrical systems. A reliable bearing
                 fault diagnostic technique is critically needed in
                 industries to recognize a bearing fault at its early
                 stage so as to prevent system's performance degradation
                 and malfunction. In this work, a genetic programming
                 based feature reconstruction approach is proposed for
                 bearing fault diagnostics. A new fitness measure is
                 proposed to improve the GP operations in feature
                 formulation. The original features are from the
                 modified kurtosis ratio and the one-scale wavelet
                 analysis. Investigation results show that the proposed
                 method is an effective feature formulation tool; the
                 reconstructed features are more robust against the
                 variations in bearing geometry and operating
                 conditions. The corresponding fault diagnostic
                 reliability can be enhanced significantly. As a result,
                 this work provides a promising technique and tool for
                 bearing condition monitoring for real-world
                 applications.",
  notes =        "Also known as \cite{4658961}",
}

Genetic Programming entries for J Liu S Ghafari W Wang Farid M Golnaraghi Fathy M Ismail

Citations