This monograph provides a tool-set for hybrid estimation that can
successfully monitor the behavior of complex artifacts with a large
number of possible operational and failure modes such as production
plants, automotive or aeronautic systems, and autonomous robots. For
this purpose, ideas from the fields of System Theory and Artificial
Intelligence are taken and hybrid estimation is reformulated as a search
problem. This allows to focus the estimation onto highly probably
operational modes, without missing symptoms that might be hidden among
the noise in the system. Additionally a novel approach to continue
hybrid estimation in the presence of unknown behavioral modes and to
automate system analysis and synthesis tasks for on-line operation are
presented. This leads to a flexible model-based hybrid estimation scheme
for complex artifacts that robustly copes with unforeseen situations.