Kernel-based fault diagnosis of inertial sensors using analytical redundancy

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

Evaluation of knowledge flow from developed to developing countries in small satellite collaborative projects: the case of Algeria

Technology transfer involves the flow of knowledge from technology developers or possessors to technology acquirers that benefit from the knowledge. This article proposes a model for the evaluation of knowledge flow in complex technology transfer projects from developed to developing countries. The proposed knowledge flow model is built by combining the concepts of knowledge viscosity and velocity with the concepts of architectural and component knowledge. The model rests on the idea that the transfer of knowledge to resource-limited organizations such as those in developing countries requires a balance between viscosity and velocity on one hand and between architectural and component knowledge on the other. The knowledge flow model has been tested on data sourced from three Earth-observation small satellite collaborative projects leveraged by Algeria to acquire small satellite technology from abroad and build local capability. The implementation of the model revealed that the collaborative projects enabled only the acquisition of a shallow form of architectural knowledge detached from the local environment. The findings are reflective of the limitations of the collaborative projects mechanism and the challenge faced by the technology acquirer to strike the appropriate component/architectural and viscosity/velocity balance