Faults diagnosis model based on artificial immunity and its application

A sort of system for faults detection and diagnosis based on the immunology principle was presented. Initial detectors were produced at random combining negative selection of self-patterns which response normal working situation of detecting objects. The learning and memory of non-self-patterns which response abnormal working situation of detecting objects were realized using the mechanism of evolution leaning based on the artificial immune theory. The corresponding zones of different faults on states space were distinguished and marked using the results of evolution learning and information warehouse of faults. Regarding the set of each era antibodys mutated in the system learning as a random series, the condition of convergence of the series and a proof were presented. The algorithm's astringency was proved. Appling the method in detection and diagnosis for faults of gear case of machine tools, the experimental results indicate that the method is effective.