Tuning and comparing fault diagnosis methods for aeronautical systems via Kriging-based optimization

International audienceMany approaches address fault detection and isolation (FDI) based on analytical redundancy. To rank them, it is necessary to define performance indices and realistic sets of test cases on which these performance indices will be evaluated. For the ranking to be fair, each of the methods under consideration should have its internal parameters (often called hyperparameters) tuned optimally. However, no mathematical model linking hyperparameters and performance is available a priori. In this paper, we propose to use a combination of tools developed in the context of computer experiments to build such a model from a limited number of numerical evaluations of the performance indices at carefully chosen values of the hyperparameters. The optimal tuning of fault diagnosis methods may prove to be strongly sensitive to specifics of the test cases. This is why the methodology is extended so as to provide a tuning that is robust to variability in the conditions of use. The performance criteria are then replaced by their worst-case values when the sources of variability are assumed to belong to some predefined sets. This methodology is applied to tune fault diagnosis approaches on an aeronautical case study