Martha A. Zaidan

TL;DR: A Bayesian Hierarchical Model is established to perform inference and inform a probabilistic model of remaining useful life for civil aerospace gas turbine engines and is compared with that of an existing Bayesian non-Hierarchical model and is found to be superior in typical (heterogeneous) scenarios.

TL;DR: Variational inference is applied to the hierarchical formulation to overcome the computational and convergence concerns that are raised by the numerical sampling techniques needed for inference in the original formulation.

TL;DR: This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy, and enables scaling-up accurate air pollution mapping appropriate for smart cities.

TL;DR: A data-driven methodology combining the hierarchical Bayesian data modelling techniques with an information-theoretic direct density ratio based change point detection algorithm to address two very generic issues namely dealing with irregular events and dealing with recoverable degradation, which are often encountered in the prognosis of complex systems such as the modern gas turbine engines are presented.

TL;DR: Prognostics is an emerging capability of modern health monitoring that aims to increase the fidelity of failure predictions as discussed by the authors, and it is a key technology to maximise aircraft availability, offering a route to increase time in-service and reduce operational disruption through improved asset management.