Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction

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@Article{Liao:2014:ieeeIE,
  author =       "Linxia Liao",
  journal =      "IEEE Transactions on Industrial Electronics",
  title =        "Discovering Prognostic Features Using Genetic
                 Programming in Remaining Useful Life Prediction",
  year =         "2014",
  month =        may,
  volume =       "61",
  number =       "5",
  pages =        "2464--2472",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TIE.2013.2270212",
  ISSN =         "0278-0046",
  abstract =     "In prognostics approaches, features (e.g., vibration
                 level, root mean square or outputs from signal
                 processing techniques) extracted from the measurement
                 (e.g., vibration, current, and pressure, etc.) are
                 often used or modelled as an indicator to the
                 equipment's health condition. When faults are detected
                 or when increasing/decreasing trends are shown in the
                 health indicator, prediction algorithms are applied to
                 extrapolate the future behaviour and predict remaining
                 useful life (RUL). However, it is difficult to make an
                 accurate prediction if the trend of the health
                 indicator is not obvious through the entire life cycle
                 or if the trend is only shown right before a failure
                 occurs. The challenge lies in whether an advanced
                 feature (e.g., a mathematical combination of a group of
                 the extracted features) can be found to clearly
                 present/correlate with the fault progression. A genetic
                 programming method is proposed to address the challenge
                 of automatically discovering advanced feature(s), which
                 can well capture the fault progression, from the
                 measurement or extracted features in the purpose of RUL
                 prediction.",
  notes =        "Also known as \cite{6544227}",
}

Genetic Programming entries for Linxia Liao

Citations