The aims are to acquaint students with the need, in various branches of engineering, to estimate the state of a dynamic system from measurements (deterministic or noisy), and also to detect the occurrence of faults and abrupt system changes, and to equip them with some of the principal techniques available for this purpose.

The course enables a student to design a state estimator in both deterministic and stochastic context. In the former case, state observers are designed in several different observability scenarios. In the latter case, the design of a 'Kalman filter' for the least squares estimation of the state of a stochastic system is dealt with. The course also enables a student to address model-based fault-diagnosis exploiting the state estimation tools in order to decide whether or not system outputs are consistent with the occurrence of a fault.

This course is concerned with the estimation and diagnosis of deterministic and stochastic linear systems with the aim to detect faults and changes. Topics covered include: models of dynamic systems and of faulty modes of behaviour. Observability properties. Design of full-order, reduced-order and unknown-input state observers. Kalman filtering: the Kalman filter, steady-state filters, the extended Kalman filter. Fault detection: design of model-based diagnosis algorithms using state estimation.