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

We propose explicit state-space based fault detection, isolation and estimation filters that are data-driven and are directly identified from only the system input-output (I/O) measurements and through the system Markov parameters. The proposed procedures do not involve a reduction step and do not require identification of the system extended observability matrix or its left null space. The performance of the proposed filters is directly connected to and linearly dependent on the errors in the Markov parameters identification process. It is shown that the system observability will suffice for characterizing the fault detection, isolation and estimation filters for both sensor faults as well as fault detection observers for the actuator faults. However, characterizing the actuator fault isolation and estimation filters is feasible only if the system is minimum phase. We have also quantified the fault estimation error in terms of the Markov parameters identification errors. 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.