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.
CITATION STYLE
Kumar, M., Jayaraman, S., Bhat, S., Ghosh, S., & Jayaraman, V. K. (2014). Variable selection and fault detection using a hybrid intelligent water drop algorithm. In Advances in Intelligent Systems and Computing (Vol. 236, pp. 225–231). Springer Verlag. https://doi.org/10.1007/978-81-322-1602-5_25
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