  This work is devoted to the development of a distributionally robust active
fault diagnosis approach for a class of nonlinear systems, which takes into
account any ambiguity in distribution information of the uncertain model
parameters. More specifically, a new approach is presented using the total
variation distance metric as an information constraint, and as a measure for
the separation of multiple models based on the similarity of their output
probability density functions. A practical aspect of the proposed approach is
that different levels of ambiguity may be assigned to the models pertaining to
the different fault scenarios. The main feature of the proposed solution is
that it is expressed in terms of distribution's first and second moments, and
hence, can be applied to alternative distributions other than normal. In
addition, necessary and sufficient conditions of optimality are derived, and an
offline active fault diagnosis procedure is provided to exemplify the
implementation of the proposed scheme. The effectiveness of the proposed
distributionally robust approach is demonstrated through an application to a
three-tank benchmark system under multiple fault scenarios.
