Genetic programming-based classification of ferrograph wear particles

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Bin Xu and Guangrui Wen and Zhifen Zhang and 
                 Feng Chen",
  booktitle =    "2016 13th International Conference on Ubiquitous
                 Robots and Ambient Intelligence (URAI)",
  title =        "Genetic programming-based classification of ferrograph
                 wear particles",
  year =         "2016",
  pages =        "842--847",
  abstract =     "Ferrograph analysis is becoming one of the principal
                 methods for condition monitoring and fault diagnosis of
                 the machinery equipment due to its advantages of
                 visualization and efficiency. One of the major
                 challenges of ferrograph analysis is feature
                 construction from the existing features of wear
                 particles to improve classifier efficiency. The current
                 feature construction method is trial and error based on
                 previous experience and mass data, which is
                 time-consuming, laborious and blindness. In this paper,
                 genetic programming-based approach was proposed to
                 construct new features from the five existing
                 morphological features of ferrograph wear particles to
                 improve the ability of classification process. The
                 GP-based feature construction approach is used for
                 fault classification of ferrograph wear particles for
                 the first time and the results show that the method can
                 be used in wear condition monitoring and fault
                 prognosis of machinery equipment.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/URAI.2016.7733992",
  month =        aug,
  notes =        "Also known as \cite{7733992}",

Genetic Programming entries for Bin Xu Guangrui Wen Zhifen Zhang Feng Chen