Robust automatic tuning of diagnosis methods via an efficient use of costly simulations

The robust tuning methodology developed in this paper aims at adjusting automatically the hyperparameters of fault-diagnosis procedures for complex case studies. The strategy should make an efficient use of computer simulations of these case studies, which will usually be computationally expensive. To this end, Kriging-based optimization is called upon. Robustness to environmental disturbances is achieved by continuous minimax optimization, and handled through an iterative relaxation procedure. This strategy is applied to the automatic tuning of a model-based fault-diagnosis scheme for a realistic aerospace application.