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An artificial immune system for adaptive fault detection, diagnosis and recovery
Chee Kiang Chun,
Published in
Volume: 4
Issue: 1-2
Pages: 22 - 31
The human immune system provides rich metaphors for adaptive pattern recognition. Fault detection and diagnosis in chemical processes is commonly formu- lated as a pattern recognition problem. However, conven- tional methods for fault diagnosis often do not have a mechanism to adapt and learn as the process changes over time. In this paper, we propose an Artificial Immune Sys- tem (AIS) framework that endows learning to statistical process monitoring techniques such as Principal compo- nent analysis. The proposed AIS framework also provides a direct means to incorporate recovery actions after a failure has been detected and diagnosed. We demonstrate the efficacy of the proposed framework using a simulated binary distillation column case study.
About the journal
JournalInternational Journal of Advances in Engineering Sciences and Applied Mathematics