An optimization approach to nonlinear diagnostic filter synthesis

We consider the synthesis problem for diagnostic filters based on nonlinear models of the systems being diagnosed. To solve the problem, we propose a new approach that unites the methods of algebra of functions and differential geometry when performing a nonlinear transformation of the original mathematical model for the diagnosed system with linear optimization techniques. An advantage of the proposed approach is that it overcomes principled obstacles of existing diagnostic filter synthesis methods and gets a solution for systems with parametric uncertainties.