School of Computer Science and Technology China University of Mining and Technology Xu Zhou, China

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Abstract—Applying artificial immune system to fault diagnosis is a new development direction in artificial intelligence, but the traditional artificial immune mode could not reasonably reflect the semantic similarity in the complexity problem space. For issues of semantic description of fault diagnosis, this paper introduces group cooperative mechanism of lymphocyte with a semantic tag to artificial immune system, thus solves the problem of semantic logical reasoning of fault knowledge. This paper presents a semantic-based artificial immune diagnosis model; designs an immune negative selection diagnostic in semantic environment; utilizes new coevolutionary algorithm diagnostic; and diagnoses fault in large electromechanical devices. The experimental results show that the method used in this paper has higher classification accuracy compared with the traditional artificial immune diagnostics, at the same time, verified expression capacity of semanticbased lymphocytes, which can provide more valuable diagnostic information.

Index Terms—Fault diagnosis; Artificial immune system; Lymphocyte; Semantics

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Cite: Chu-jiao Wang, Shi-xiong Xia, and Yong Zhou, " Semantic Artificial Immune Model for Fault Diagnosis," Journal of Computers vol. 8, no. 8, pp. 2059-2068, 2013.