Abstract
This paper explores the application of bioinspired cooperative strategies for optimization on Fault Diagnosis in industrial systems. As a first step, the Differential Evolution and Ant Colony Optimization algorithms are considered. Both algorithms have been applied to a benchmark problem, the two tanks system. The experiments have considered noisy data in order to compare the robustness of the diagnosis. The preliminary results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for the Fault Detection and Isolation problem.
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Echevarría, L.C., Llanes-Santiago, O., da Silva Neto, A.J. (2010). Fault Diagnosis in Industrial Systems Using Bioinspired Cooperative Strategies. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_5
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DOI: https://doi.org/10.1007/978-3-642-12538-6_5
Publisher Name: Springer, Berlin, Heidelberg
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