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A new fault detection method based on artificial immune systems
Author(s) -
Wang Cunjie,
Zhao Yuhong
Publication year - 2008
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.208
Subject(s) - detector , artificial immune system , clonal selection , fault detection and isolation , principal component analysis , feature selection , process (computing) , set (abstract data type) , fault (geology) , artificial intelligence , computer science , pattern recognition (psychology) , selection (genetic algorithm) , clonal selection algorithm , negative selection , algorithm , telecommunications , biochemistry , chemistry , actuator , operating system , genome , seismology , gene , immunology , biology , programming language , geology
Abstract A new fault detection method with a continuous learning feature for a complicated process is proposed based on the concept of artificial immune systems (AIS). Both the negative and the clonal selections are adopted in the method. The real‐valued negative selection algorithm (RNSA) is utilized to generate fault detectors. When the detector set is used to perform the fault detection, a clonal selection is employed to update the fault detector set. The proposed method is applied to the Tennessee Eastman (TE) process. The simulation results show that the performance of the proposed method is superior to those of both classical principal component analysis (PCA) and negative selection algorithm. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd.