Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering
Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering
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DOI：
10.1016/j.eswa.2012.04.066
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Guilherme Costa Silva;R. Palhares;W. Caminhas
中科院分区：
文献类型：
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作者：
Guilherme Costa Silva;R. Palhares;W. Caminhas
本文描述了一种基于自我非自我区分理论的免疫激发系统，该理论将负选择过程定义为基于抗原与t细胞亲和力的模糊系统机制。该理论可以提供一种决策工具，改进检测器的生成，甚至定义新的数据监测，以检测系统行为的极端变化，这意味着异常情况的发生。通过这些算法，进行了直流电机故障检测试验。在检测到故障后，使用参与式聚类算法对这些故障进行分类并进行测试，以获得最佳参数集，从而在本文讨论的应用程序中为这些测试实现最准确的聚类。
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.