MATLAB Implementation of Artificial Immune Algorithm for Fault Diagnosis

This article presents a MATLAB implementation of the artificial immune algorithm and explores its applications in fault diagnosis. The artificial immune algorithm is a computational model inspired by the human immune system, capable of simulating natural immune mechanisms including learning, recognition, and memory processes to effectively solve various optimization problems. In fault diagnosis applications, the algorithm typically involves implementing antibody-antigen recognition mechanisms using mathematical models such as affinity calculation functions, where antibody populations evolve through cloning, mutation, and selection operations. Key MATLAB implementations may include functions for initializing immune cells, calculating affinity matrices, performing clonal selection, and implementing memory cell updates. The algorithm can learn and identify system characteristics through pattern matching and optimization processes, enabling automatic fault detection and providing effective solutions. This paper aims to introduce the MATLAB-based artificial immune algorithm implementation and discuss its fault diagnosis applications, providing valuable references for researchers in related fields.