  In this work, we propose explicit state-space based fault detection,
isolation and estimation filters that are data-driven and are directly
identified and constructed from only the system input-output (I/O) measurements
and through estimating the system Markov parameters. The proposed methodology
does not involve a reduction step and does not require identification of the
system extended observability matrix or its left null space. The performance of
our proposed filters is directly connected to and linearly dependent on the
errors in the Markov parameters identification process. The estimation filters
operate with a subset of the system I/O data that is selected by the designer.
It is shown that the proposed filters provide asymptotically unbiased estimates
by invoking low order filters as long as the selected subsystem has a stable
inverse. We have derived the estimation error dynamics in terms of the Markov
parameters identification errors and have shown that they can be directly
synthesized from the healthy system I/O data. Consequently, the estimation
errors can be effectively compensated for. Finally, we have provided several
illustrative case study simulations that demonstrate and confirm the merits of
our proposed schemes as compared to methodologies that are available in the
literature.
