  In this article, we propose a tractable nonlinear fault isolation filter
along with explicit performance bounds for a class of nonlinear dynamical
systems. We consider the presence of additive and multiplicative faults,
occurring simultaneously and through an identical dynamical relationship, which
represents a relevant case in several application domains. The proposed filter
architecture combines tools from model-based approaches in the control
literature and regression techniques from machine learning. To this end, we
view the regression operator through a system-theoretic perspective to develop
operator bounds that are then utilized to derive performance bounds for the
proposed estimation filter. In the case of constant, simultaneously and
identically acting additive and multiplicative faults, it can be shown that the
estimation error converges to zero with an exponential rate. The performance of
the proposed estimation filter in the presence of incipient faults is validated
through an application on the lateral safety systems of SAE level 4 automated
vehicles. The numerical results show that the theoretical bounds of this study
are indeed close to the actual estimation error.
