Abstract: In this paper, a geometric approach to fault detection and isolation (FDI) is applied to a Multiple-Input Multiple-Output (MIMO) model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output (SISO), Multiple-Input Single-Output (MISO), and Single-Input Multiple-Output (SIMO) cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX (Auto Regressive with eXogenous input) and VARX (Vector ARX) models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.

Key words: Fault detection and isolation (FDI), Multivariate systems, Parametric system identification, Linear regression, Distance functions

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