Intelligent engine condition monitoring and early fault detection is becoming a necessity for modern gas turbines to achieve availability and reliability requirements. Degradation or failure of critical control components negatively affects reliability and safety as well as the ability to perform condition monitoring of the engine. Model-based approaches for analytics and condition monitoring show great promise with advances in remote connectivity and available computational power. Development of diagnostics for analytics requires focus on the target machine as well as the integrity of measurement and actuation systems to correctly identify and classify degradation indicators that discriminate between actuation and measurement faults from deterioration in machine performance. In this paper, a method is proposed to use data obtained in closed-loop operation to identify system models that separate the dynamic responses and non-linear characteristics for use in analytics. This is demonstrated on high-fidelity simulation data from a Taurus 60 gas turbine generator. The system is modeled with a feedback connection of a known controller in series with a block Hammerstein system.

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