Fault Detection Method Based on Artificial Immune System for Complicated Process
Fault detection is an important problem in process engineering. A new fault detection method based on artificial immune system is developed for complicated process. Real-valued negative selection algorithm with variable–radius detectors is adopted to generate the detectors set which covers the non-self space. In order to decrease the complexity of detector generation, principal component analysis is introduced to reduce the dimension of the process data. The effectiveness of the proposed method is illustrated by the simulation on the Tennessee Eastman process.
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