| Summary: | This thesis examines the applicability of artificial neural networks (neural computation methods) to tasks of forecasting and condition monitoring involved real-world data in the context of a large airline business. The first chapter introduces artificial neural networks (concentrating on multilayer perceptron, radial basis function and self-organising map networks), and provides some motivation for their use in problems of statistical estimation and monitoring. the second chapter gives a case study of the estimation of airline booking take-up from booking attributes held in a reservations system, comparing multilayer perceptrons and radial basis function networks, used in a classification regime, with the statistical method of Automatic Interaction Detection (AID) and lookup table and moving average methods. Some consideration is given to how the outputs of the neural network should be interpreted and to application-specific issues. The third chapter introduces the task of aircraft engine condition monitoring, gives a literature survey of the use of neural networks and related methods in condition monitoring and considers the applicability of various neural network approaches to the aircraft engine monitoring problem. In the fourth chapter a method of context-based novelty detection using a combination of self-organising maps is developed, and this, together with classification and regression approaches to the tasks of condition monitoring and fault detection using artificial neural networks are illustrated with a series of case studies. The fifth chapter gives concluding remarks on the use of artificial neural networks for data-driven forecasting and monitoring tasks using operational data, and briefly considers software engineering and methodological issues.
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