Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering

This paper describes an immune-inspired system based on an alternate theory about the self–nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even define new data monitoring in order to detect an extreme variation of the system behavior, which means anomalies occurrences. Through these algorithms, tests are performed to detect faults of a DC motor. Upon detection of faults, a participatory clustering algorithm is used to classify these faults and tested to obtain the best set of parameters to achieve the most accurate clustering for these tests in the application being discussed in the article.

► An immune-inspired system based on fuzzy antigen recognition is presented.
► The fuzzy antigen recognition improves performance on detector generating algorithms.
► A monitoring algorithm using distance measures and the fuzzy system is proposed.
► For fault distinction, a participatory clustering algorithm is used.