Kernel-based fault diagnosis of inertial sensors using analytical redundancy

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dc.contributor.advisor Aouf, Nabil Vitanov, Ivan V. 2017-12-13T11:12:46Z 2017-12-13T11:12:46Z 2017
dc.description.abstract Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution. en_UK
dc.language.iso en en_UK
dc.relation.ispartofseries CDS PhD Thesis;PHD-17-VITANOD
dc.rights © Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.
dc.subject Fault detection en_UK
dc.subject Signal processing en_UK
dc.title Kernel-based fault diagnosis of inertial sensors using analytical redundancy en_UK
dc.type Thesis en_UK

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