Optimum input design for fault detection and diagnosis: Model-based prediction and statistical distance measures

This paper proposes optimization-based active fault detection and diagnosis (FDD) methods. An optimal input sequence is computed for maximizing discrimination between system models of fault scenarios in a statistical sense. Two different measures quantifying the degree of distinguishability between two stochastic LTI system models are considered, and their geometric properties are investigated. Their connection to the generalized likelihood ratio tests are also presented. Constrained open- and closed-loop feedback input design methods using model-based prediction are presented. Constraints on the predicted controlled output trajectory are imposed for ensuring operational safety as well as the input constraints that correspond to hardware limitations. Receding horizon method is used to implement the computed inputs.