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Accurate prognostic models and associated algorithms that are capable of predicting future component failure rates or performance degradation rates for shipboard propulsion systems are critical for optimizing the timing of recurring maintenance actions. As part of the Naval maintenance philosophy on Condition Based Maintenance (CBM), prognostic algorithms are being developed for gas turbine applications that utilize state-of-the-art probabilistic modeling and analysis technologies. Naval Surface Warfare Center, Carderock Division (NSWCCD) Code 9334 has continued interest in investigating methods for implementing CBM algorithms to modify gas turbine preventative maintenance in such areas as internal crank wash, fuel nozzles and lube oil filter replacement. This paper will discuss a prognostic modeling approach developed for the LM2500 and Allison 501-K17 gas turbines based on the combination of probabilistic analysis and fouling test results obtained from NSWCCD in Philadelphia. In this application, the prognostic module is used to assess and predict compressor performance degradation rates due to salt deposit ingestion. From this information, the optimum time for on-line waterwashing or crank washing from a cost/benefit standpoint is determined. NOMENCLATURE

The benefits of utilizing Condition Based Maintenance (CBM) over a time based preventative approach to reduce life cycle costs has been well documented over the years. The U.S. Navy has been working through several separate programs, such as Smartship, Integrated Condition Assessment System (ICAS), Accelerated Capabilities Initiative (ACI), to develop this technology for use in the fleet. Recently, an effort was initiated to coordinate these programs in an application on marine gas turbines. This paper describes the phased process of CBM implementation on marine gas turbines and the resulting benefits of reduced life cycle costs and condition based engineering plant alignment.

As part of the Naval gas turbine CBM effort, diagnostic and prognostic algorithms that utilize state-of-the-art probabilistic modeling and analysis technologies are being developed and implemented onboard Navy ships. The algorithms under development and testing will enhance gas turbine preventative maintenance in such areas as compressor on-line/crank wash and fuel nozzle replacement. In one application, the prognostic module assesses and predicts compressor performance degradation due to salt ingestion. From this information, the optimum time for on-line water washing or crank washing can be determined from a cost/benefit standpoint. A second application diagnoses the severity of fuel nozzle fouling in real-time during startup. This paper discusses the diagnostic and prognostic modeling approaches to these maintenance issues and their implementation for an Allison 501-K34 gas turbine engine onboard a DDG 51 class guided missile destroyer.