An evolutionary based clustering algorithm applied to Dada compression for industrial systems

Chen, Jun and Mahfouf, Mahdi and Bingham, Chris and Zhang, Yu and Yang, Zhijing and Gallimore, Michael (2012) An evolutionary based clustering algorithm applied to Dada compression for industrial systems. In: 11th International Symposium, IDA 2012, October 25-27, 2012, Helsinki, Finland.

Full content URL: http://dx.doi.org/10.1007/978-3-642-34156-4_11

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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
Subjects:H Engineering > H660 Control Systems
H Engineering > H650 Systems Engineering
Divisions:College of Science > School of Engineering
ID Code:14796
Deposited On:07 Sep 2014 20:47

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