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

Chen, Jun, Mahfouf, Mahdi, Bingham, Chris , Zhang, Yu, 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.

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


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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