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Variable selection and fault detection using a hybrid intelligent water drop algorithm
Kumar M., Jayaraman S., Bhat S., Ghosh S.,
Published in Springer Verlag
Volume: 236
Pages: 225 - 231
Process fault detection concerns itself with monitoring process variables and identifying when a fault has occurred in the process workflow. Sophisticated learning algorithms may be used to select the relevant process state variables out of a massive search space and can be used to build more efficient and robust fault detection models. In this study, we present a recently proposed swarm intelligencebased hybrid intelligent water drop (IWD) optimization algorithm in combination with support vector machines and an information gain heuristic for selecting a subset of relevant fault indicators. In the process, we demonstrate the successful application and effectiveness of this swarm intelligence-based method to variable selection and fault identification. Moreover, performance testing on standard machine learning benchmark datasets also indicates its viability as a strong candidate for complex classification and prediction tasks. © Springer India 2014.
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Published in Springer Verlag
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