OPTIMAL UNKNOWN INPUT DISTRIBUTION MATRIX SELECTION IN ROBUST FAULT-DIAGNOSIS

Uncertainties in dynamic systems are an inevitable consequence of non-linearity and complexity, and obscure the performance of fault diagnosis. In order to achieve robust and reliable fault diagnosis, the unknown input (disturbance) de-coupling principle has been employed in recent research. In this paper, a method of computing the unknown input distribution matrix is proposed as a powerful alternative method to either re-identification of plant parameters arising from different operating points or to the use of non-linear residual generation. The determination of a suitable unknown input distribution matrix to achieve disturbance de-coupling is described as an optimization problem which is solved here via a Singular Value Decomposition. An example of robust fault detection applied to a jet engine system is included as an illustration.