  We propose a systematic method to directly identify a sensor fault estimation
filter from plant input/output data collected under fault-free condition. This
problem is challenging, especially when omitting the step of building an
explicit state-space plant model in data-driven design, because the inverse of
the underlying plant dynamics is required and needs to be stable. We show that
it is possible to address this problem by relying on a system-inversion-based
fault estimation filter that is parameterized using identified Markov
parameters. Our novel data-driven approach improves estimation performance by
avoiding the propagation of model reduction errors originating from
identification of the state-space plant model into the designed filter.
Furthermore, it allows additional design freedom to stabilize the obtained
filter under the same stabilizability condition as the existing model-based
system inversion. This crucial property enables its application to sensor
faults in unstable plants, where existing data-driven filter designs could not
be applied so far due to the lack of such stability guarantees (even after
stabilizing the closed-loop system). A numerical simulation example of sensor
faults in an unstable aircraft system illustrates the effectiveness of the
proposed new method.
