Machinery time to failure prediction - Case study and lesson learned for a spindle bearing application

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Liao:2013:PHM,
  author =       "Linxia Liao and Radu Pavel",
  booktitle =    "IEEE Conference on Prognostics and Health Management
                 (PHM 2013)",
  title =        "Machinery time to failure prediction - Case study and
                 lesson learned for a spindle bearing application",
  year =         "2013",
  month =        "24-27 " # jun,
  keywords =     "genetic algorithms, genetic programming, spindle
                 bearing, time to failure, predictive analytics",
  DOI =          "doi:10.1109/ICPHM.2013.6621416",
  abstract =     "One of the important roles of prognostics health
                 management (PHM) is to predict the time to failure of a
                 system in order to avoid unexpected downtime and
                 optimise maintenance activities. Although many attempts
                 to predict time to failure have been reported in the
                 literature, there are still challenges related to data
                 availability and methodology. In addition, there is
                 significant variation from case to case due to
                 complexity of system usage and failure modes. This
                 paper reveals various aspects related to such
                 challenges experienced while applying a novel
                 predictive technology to a spindle test-bed. The goal
                 was to evaluate the ability of the technology to
                 predict the remaining useful life of a bearing with
                 seeded faults. Testing has been conducted to reveal the
                 effectiveness of signal processing, health modelling
                 and prediction techniques. While conducting the
                 evaluation tests, besides some well-known bearing
                 failure modes, an unusual case was recorded. This
                 atypical bearing failure mode created a new challenge
                 for the predictive technology being investigated, which
                 prompted the development of an advanced feature
                 discovering methodology using genetic programming. This
                 new methodology and the technology evaluation results
                 obtained for both the well-known and the atypical
                 failure modes will be discussed in the paper. In
                 addition, the paper will describe the test-bed and
                 instrumentation approach, the data acquisition system
                 and the experimental design for testing and validation
                 of the technology.",
  notes =        "Also known as \cite{6621416}",
}

Genetic Programming entries for Linxia Liao Radu Pavel

Citations