In the field of gas turbine diagnostics, a significant gap is observed between the variety of proposed and investigated diagnostic solutions and a narrow group of algorithms employed in real monitoring systems. One of the explanations is that diagnostic algorithms of a monitoring system (system components) are usually developed and verified separately from each other. An additional explanation is related to simplified simulated data used to verify the algorithms.

The present paper aims to adjust and validate the joint operation of the algorithms of a gas turbine monitoring system during the whole lifetime. The software tool called the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) provides the input data. This tool realistically simulates the steady-state parameters of an aircraft engine fleet. The simulation embraces a total engine life and includes different variations of deterioration and various engine, actuator, and sensor faults.

Using ProDiMES, some diagnostic solutions have been verified so far. However, they do not include all necessary monitoring system components, use a short fixed-time interval of input data, and do not analyze long-term deterioration. In contrast, this paper presents an attempt to enhance a whole diagnostic process. It considers the operation of various data-driven algorithms of a monitoring system during engine life. The paper focuses on the tuning of the algorithms themselves and adjustment of their interactions.

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