Extrinsic curvature-based fault isolation for multi-input—multi-output systems with non-linear fault models

Abstract

This paper presents an online fault isolation methodology for identifying faulty signals in a multiinput—multi-output dynamical system. It is hypothesized that faults in a dynamical system can be suitably represented via non-linear functions. The isolation scheme, which is implemented online, relies on adaptive non-linear estimates of these non-linear fault functions based on the system input—output data. The non-linear fault estimation is achieved using radial basis function neural network (RBFNN) architecture while the fault isolation is accomplished using extrinsic curvature of the learned RBFNN model. The proposed approach is implemented on an F-5A Freedom Fighter aircraft's lateral-directional model and the results are presented to illustrate the concept.