UC San Diego

This dissertation is a repository of techniques employed in the health management of industrial gas turbines. The first chapters cover methods of diagnostics using statistics and machine learning algorithms to identify deviations from normal operation. The middle chapters cover applications of prognostics in the sense of generating predictions of various sensor values of interest and comparing predictions with observed values through residual analysis. The later chapters develop applications of optimization techniques to the estimation of compressor degradation rates and to the creation of optimized maintenance schedules. The final chapter takes a more scrutinizing look at the mathematics of estimating piecewise linear functions and proves a result about minimal cardinality. Taken in whole, this work covers a broad spectrum from applications to theory of remote health management of Gas Turbines, and generally, rotating machines.