Real Time Prognostic Strategies with Application to Stationary Gas Turbines

Abstract Stationary gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. The efficiency of these complex multi-domain systems is dependent on the turbine's design, established operating envelope, environmental conditions, and maintenance schedule. A real time health management strategy can enhance overall plant reliability through the continual monitoring of transient and steady-state system operations. The availability of sensory information for control system needs often allow diagnostic/prognostic algorithms to be executed in a parallel fashion which warn of impending system degradations. Specifically, prognostic strategies estimate the future plant behavior which leads to minimized maintenance costs through timely repairs, and hence, improved reliability. In this paper, statistical and wavelet prognostic methods are presented to forecast system health. For the statistical approach, a multidimensional empirical description reveals dominant data trends and estimates future behavior. The wavelet approach uses second order Daubechies wavelet coefficients to generate signal approximations that forecast future plant operation. Experimental data has been collected on a Solar Mercury 50 stationary gas turbine. The monitored plant signals were analyzed to identify prognostic information for preventative action recommendations. Representative results are presented and discussed to compare the overall performance of each prognostic algorithm