Improved fault diagnosis algorithm based on artificial immune network model and neighbourhood rough set theory. Issue 4 (1st July 2021)

• Record Type: Journal Article
• Authors: Zheng, Yonghuang; Li, Benhong; Zhang, Shangmin
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 4(2021)
• Page Start: 323
• Page End: 331
• Publication Date: 2021-07-01
• Subjects: fault diagnosis -- artificial immune systems -- rough set theory; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12026
• Languages: English
• ISSNs: 2517-7567

Abstract

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.