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In this work, we propose explicit state-space based fault detection,isolation and estimation filters that are data-driven and are directlyidentified and constructed from only the system input-output (I/O) measurementsand through estimating the system Markov parameters. The proposed methodologydoes not involve a reduction step and does not require identification of thesystem extended observability matrix or its left null space. The performance ofour proposed filters is directly connected to and linearly dependent on theerrors in the Markov parameters identification process. The estimation filtersoperate 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 estimatesby invoking low order filters as long as the selected subsystem has a stableinverse. We have derived the estimation error dynamics in terms of the Markovparameters identification errors and have shown that they can be directlysynthesized from the healthy system I/O data. Consequently, the estimationerrors can be effectively compensated for. Finally, we have provided severalillustrative case study simulations that demonstrate and confirm the merits ofour proposed schemes as compared to methodologies that are available in theliterature.

A Data-driven Approach to Actuator and Sensor Fault Detection, Isolation and Estimation in Discrete-Time Linear Systems