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Modern health management approaches for gas turbine engines (GTEs) aim to precisely estimate the health state of the GTE components to optimize maintenance decisions with respect to both economy and safety. In this research, we propose an advanced framework to identify the most likely degradation state of the turbine section in a GTE for prognostics and health management (PHM) applications. A novel nonlinear thermodynamic model is used to predict the performance parameters of the GTE given the measurements. The ratio between real efficiency of the GTE and simulated efficiency in the newly installed condition is defined as the health indicator and provided at each sequence. The symptom of nonrecoverable degradations in the turbine section, i.e. loss of turbine efficiency, is assumed to be the internal degradation state. A regularized auxiliary particle filter (RAPF) is developed to sequentially estimate the internal degradation state in nonuniform time sequences upon receiving sets of new measurements. The effectiveness of the technique is examined using the operating data over an entire time-between-overhaul cycle of a simple-cycle industrial GTE. The results clearly show the trend of degradation in the turbine section and the occasional fluctuations, which are well supported by the service history of the GTE. The research also suggests the efficacy of the proposed technique to monitor the health state of the turbine section of a GTE by implementing model-based PHM without the need for additional instrumentation.

Gas turbines are often used under variable ambient conditions and power demands, which also may be off their design points. Such operating scenarios affect the typical performance parameters, such as thermal efficiency, mass flow and power. As a result, such parameters may fail to accurately indicate the structural degradation of a gas turbine. The objective of this study is to develop a robust physics-based performance indicator for a gas turbine to demonstrate the short term recoverable as well as long term non-recoverable degradation level of the engine, independent of the operating conditions. A comprehensive physics-based thermodynamic model for the gas path of a single shaft gas turbine is developed to accurately predict the cycle parameters based on limited actual operating data. Consequently, for the given ambient condition, demanded power and shaft speed, the model predicts the cycle parameters for the gas turbine in a healthy condition as the baseline. In reality, the measured parameters gradually deviate from the model, which reflects the performance deterioration of the engine caused by degradation mechanisms. In order to capture this performance deterioration, the ratio of the excess exhaust heat power with respect to the design point power, called Excess Heat Ratio (EH) is being proposed as an effective indicator. The effectiveness of the Excess Heat Ratio is examined by using 38-month operating data of an industrial gas turbine between two major overhauls. The trends of EH clearly shows its capability to capture the short term recoverable degradations and subsequent retrievals, arising from compressor fouling and subsequent wash. In addition, EH is also able to capture the trend of the long term non-recoverable degradations. The proposed indicator has the following advantages: 1) only limited data from the operating system of a gas turbine is required without the need of additional instrumentation; 2) the both short and long term degradations of the gas turbine can be quantified by a single indicator that is independent from the operating conditions; and 3) it is practically applicable for real-time monitoring and maintenance planning.