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In this article, we propose a tractable nonlinear fault isolation filteralong with explicit performance bounds for a class of nonlinear dynamicalsystems. We consider the presence of additive and multiplicative faults,occurring simultaneously and through an identical dynamical relationship, whichrepresents a relevant case in several application domains. The proposed filterarchitecture combines tools from model-based approaches in the controlliterature and regression techniques from machine learning. To this end, weview the regression operator through a system-theoretic perspective to developoperator bounds that are then utilized to derive performance bounds for theproposed estimation filter. In the case of constant, simultaneously andidentically acting additive and multiplicative faults, it can be shown that theestimation error converges to zero with an exponential rate. The performance ofthe proposed estimation filter in the presence of incipient faults is validatedthrough an application on the lateral safety systems of SAE level 4 automatedvehicles. The numerical results show that the theoretical bounds of this studyare indeed close to the actual estimation error.

Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds