Open AccessImproved fault diagnosis algorithm based on artificial immune network model and neighbourhood rough set theory
Author(s) -
Zheng Yonghuang,
Li Benhong,
Zhang Shangmin
Publication year - 2021
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12026
Subject(s) - neighbourhood (mathematics) , rough set , pruning , algorithm , fault (geology) , computer science , artificial intelligence , artificial immune system , pattern recognition (psychology) , mathematics , mathematical analysis , seismology , agronomy , biology , geology
Abstract With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low.