In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified.

中文翻译：

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树突状细胞免疫系统启发方案的风力发电机组传感器故障检测和隔离。

在本文中，开发并实现了一种基于免疫系统（IS）激发机制的故障检测与隔离（FDI）方法，该机制被称为树突状细胞算法（DCA）。然后，将我们提出的基于DCA的FDI方法应用于著名的风力涡轮机测试模型。提出的基于DCA的方案在给定双传感器冗余的情况下既可以进行检测又可以隔离传感器故障，这与文献中仅解决故障检测问题并依靠分析冗余方法来完成故障隔离任务的其他工作不同。还执行非参数统计比较测试，以比较基于DCA的FDI方案与另一种基于IS的方案（称为否定选择算法）的性能。