Abstract
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 intelligence-based 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.
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Acknowledgments
VKJ gratefully acknowledges the Council for Scientific and Industrial Research (CSIR) and Department of Science and Technology (DST), New Delhi, India, for financial support in the form of Emeritus Scientist Grant. The authors also acknowledge the Centre for Modeling and Simulation, University of Pune and C-DAC, Pune, for their kind support.
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Kumar, M., Jayaraman, S., Bhat, S., Ghosh, S., Jayaraman, V. (2014). Variable Selection and Fault Detection Using a Hybrid Intelligent Water Drop Algorithm. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_25
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DOI: https://doi.org/10.1007/978-81-322-1602-5_25
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