Fault Diagnosis System Implemented with Immune Algorithm

This text describes a fault diagnosis program implemented using immune algorithm, which demonstrates exceptional accuracy, though its dissemination status remains unclear. The immune algorithm is a computational model inspired by biological immune system principles, characterized by strong optimization capabilities applicable across various domains including fault diagnosis. Given the critical importance of diagnostic accuracy for maintaining equipment reliability, immune algorithm-based fault diagnosis systems hold significant value. The implementation typically involves key components such as antigen-antibody representation for problem encoding, affinity calculation using similarity metrics, and clonal selection mechanisms for solution optimization. Core functions might include antibody initialization (generating candidate solutions), affinity evaluation (fitness calculation), and memory cell updating (preserving optimal solutions). If such programs haven't achieved widespread adoption, their potential remains underexploited. Therefore, proactive measures should be taken to promote their application in relevant industrial sectors, maximizing their societal impact through features like adaptive threshold setting and dynamic antibody diversity maintenance.