An Evolutionary Based Clustering Algorithm Applied to Dada Compression for Industrial Systems

  • Jun Chen
  • Mahdi Mahfouf
  • Chris Bingham
  • Yu Zhang
  • Zhijing Yang
  • Michael Gallimore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

Abstract

In this paper, in order to address the well-known ‘sensitivity’ problems associated with K-means clustering, a real-coded Genetic Algorithms (GA) is incorporated into K-means clustering. The result of the hybridisation is an enhanced search algorithm obtained by incorporating the local search capability rendered by the hill-climbing optimisation with the global search ability provided by GAs. The proposed algorithm has been compared with other clustering algorithms under the same category using an artificial data set and a benchmark problem. Results show, in all cases, that the proposed algorithm outperforms its counterparts in terms of global search capability. Moreover, the scalability of the proposed algorithm to high-dimensional problems featuring a large number of data points has been validated using an application to compress field data sets from sub-15MW industry gas turbines, during commissioning. Such compressed field data is expected to result in more efficient and more accurate sensor fault detection.

Keywords

hybridised clustering algorithm genetic algorithms K-means algorithms data compression sensor fault detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jun Chen
    • 1
  • Mahdi Mahfouf
    • 2
  • Chris Bingham
    • 1
  • Yu Zhang
    • 1
  • Zhijing Yang
    • 1
  • Michael Gallimore
    • 1
  1. 1.School of EngineeringUniversity of LincolnLincolnU.K.
  2. 2.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldU.K.

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