IJCAS

This study addresses methods for detection of faults in dynamic systems that can be represented as rigid bodies. We propose an online Gaussian process regression (GPR) re-initialization method for fault conditions, accomplished by detecting faults using a kernel linear independence test. The KLI test evaluates whether new input data shares the nominal dynamics represented by previous data points. Re-initialization of GPR is triggered by the KLI test results, enabling online GPR for real-time applications. We validated our method by simulating the generic transport model (GTM) of a fixed-wing aircraft, developed by NASA, focusing on scenarios with severed left-wing configurations.

Keywords: Fault detection, Gaussian process regression (GPR), kernel linear independence test (KLI), reproducing kernel Hilbert space (RKHS).