Model development based on evolutionary framework for condition monitoring of a lathe machine

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@Article{Garg:2015:Measurementa,
  author =       "Akhil1 Garg and V. Vijayaraghavan and K. Tai and 
                 Pravin M. Singru and Vishal Jain and 
                 Nikilesh Krishnakumar",
  title =        "Model development based on evolutionary framework for
                 condition monitoring of a lathe machine",
  journal =      "Measurement",
  volume =       "73",
  pages =        "95--110",
  year =         "2015",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2015.04.025",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224115002389",
  abstract =     "The present work deals with the vibro-acoustic
                 condition monitoring of the metal lathe machine by the
                 development of predictive models for the detection of
                 probable faults. Firstly, the experiments were
                 conducted to obtain vibration and acoustic signatures
                 for the three operations (idle running, turning and
                 facing) used for three experimental studies (overall
                 acoustic, overall vibration and headstock vibration).
                 In the perspective of formulating the predictive
                 models, multi-gene genetic programming (MGGP) approach
                 can be applied. However, it is effective functioning
                 exhibit high dependence on the complexity term
                 incorporated in its fitness function. Therefore, an
                 evolutionary framework of MGGP based on its new
                 complexity measure is proposed in formulation of the
                 predictive models. In this proposed framework,
                 polynomials known for their fixed complexity (order of
                 polynomial) are used for defining the complexity of the
                 generated models during the evolutionary stages of
                 MGGP. The new complexity term is then incorporated in
                 fitness function of MGGP to penalize the fitness of
                 models. The results reveal that the proposed models
                 outperformed the standardized MGGP models. Further, the
                 parametric and sensitivity analysis is conducted to
                 study the relationships between the key process
                 parameters and to reveal dominant input process
                 parameters.",
  keywords =     "genetic algorithms, genetic programming, Vibration,
                 Acoustics, Condition monitoring, Machine learning,
                 Predictive maintenance, Machining modelling",
}

Genetic Programming entries for Akhil Garg Venkatesh Vijayaraghavan Kang Tai Pravin M Singru Vishal Jain Nikilesh Krishnakumar

Citations