A Framework of Design Weakness Detection through Machine Health Monitoring for the Evolutionary Design Optimization of Multi-domain Systems

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

@InProceedings{Xia:2014:ICCSE,
  author =       "Min Xia and Clarence W. {De Silva}",
  title =        "A Framework of Design Weakness Detection through
                 Machine Health Monitoring for the Evolutionary Design
                 Optimization of Multi-domain Systems",
  booktitle =    "9th International Conference on Computer Science
                 Education (ICCSE 2014)",
  year =         "2014",
  month =        aug,
  pages =        "205--210",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICCSE.2014.6926455",
  size =         "6 pages",
  abstract =     "Design of a multi-domain engineering system can be
                 complicated due to its complex structure and dynamic
                 coupling between domains. Ideally, designing a
                 multi-domain system should be done in an integrated and
                 concurrent manner, where dynamic interactions between
                 domains in the entire system have to be considered
                 simultaneously, throughout the design process. In
                 recent years, researchers have made some progress in
                 the integrated and optimal design of multi-domain
                 systems. Dynamic modelling tools such as Bond Graphs
                 and Linear Graphs have been considered for modelling
                 multi-domain systems, which can facilitate the design
                 process. In the process of design optimisation, a
                 rather challenging task is to concurrently satisfy
                 multiple design objectives. Methods of evolutionary
                 computing, genetic programming in particular, have
                 received much attention in recent years for application
                 in design optimisation. These methods can be extended
                 to evolutionary optimisation, which may involve complex
                 and non-analytic objective functions and a variety of
                 design specifications. More recently, machine health
                 monitoring system (MHMS) has been considered for
                 integration into the scheme of design evolution even
                 though no concrete developments have made in this
                 regard. In this paper, a framework of design weakness
                 detection through machine health monitoring for
                 evolutionary design optimisation of multi-domain system
                 is proposed. MHMS is integrated with evolutionary
                 design optimisation to make the overall process of
                 design evolution more effective and feasible from the
                 practical point of view. Information form MHMS is used
                 to detect the sites or candidates of design weakness,
                 which will involve computation of a new measure that
                 can reflect the quality of the current design. These
                 candidates of design weakness are then provided to the
                 process of evolutionary design optimisation. On
                 subsequent analysis, design improvements would be made
                 only if these candida- es were found to be related to
                 design weaknesses. Otherwise, the monitoring process
                 will continue. Supervised design weakness detection is
                 achieved through the integrated system of MHMS and
                 evolutionary design optimisation. In addition, a Design
                 Expert System is employed to monitor and assist both
                 design weakness detection and isolation, and feasible
                 design selection.",
  notes =        "Dept. of Mech. Eng., Univ. of British Columbia,
                 Vancouver, BC, Canada

                 Also known as \cite{6926455}",
}

Genetic Programming entries for Min Xia Clarence W de Silva

Citations