Prognostic Enhancements to Gas Turbine Diagnostic Systems

The development of machinery health monitoring technologies has taken center stage within the DoD community in recent years. Existing health monitoring systems, such as the Integrated Condition Assessment System ICAS for NAVSEA, enable the diagnosis of mission critical problems using fault detection and diagnostic technologies. These technologies, however, have not specifically focused on the automated prediction of future condition prognostics of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. Current efforts are focused on developing a generic architecture for the development of prognostic systems that will enable plug and play capabilities within existing systems. The designs utilize Open System Architecture OSA guidelines, such as OSA-CBM Condition Based Maintenance, to provide these capabilities and enhance reusability of the software modules. One such implementation, which determines the optimal water wash interval to mitigate gas turbine compressor performance degradation due to salt deposit ingestion, is the focus of this paper. The module utilizes advanced probabilistic modeling and analysis technologies to forecast the future performance characteristics of the compressor and yield the optimal Time To Wash TTW from a costbenefit standpoint. This paper describes the developed approach and architecture for developing prognostics using the gas turbine module.