Gas path analysis (GPA) is a powerful tool to predict gas turbine degradations based on measurement parameters of gas turbine engines. Accordingly, prudent measurement selections are crucial to ensure accurate GPA predictions. This paper is intended to investigate the infiuence of measurement parameter selection toward the effectiveness of GPA algorithm. An analytical methodology for measurement selection, combined with measurement subset concept, is developed to properly select measurements for multiple component fault diagnosis. The effectiveness of GPA using the measurement sets selected with the introduced measurement selection method are then compared with the results of using standard measurements installed on existing gas turbine engines. A case study applying the new measurement selection method to GPA diagnostic analysis is demonstrated on a three-shaft aeroderivative industrial gas turbine model based on similar unit in.'italled onboard an offshore platform operated by PETRONAS. The engine is modeled and simulated using PYTHIA, a gas turbine performance and diagnostics analysis tool developed by Cranfieid University. To validate the findings, nonlinear GPA prediction errors are evaluated in various cases of single and multiconiponents faults. As a result, the selected measurements have successfully produced much superior diagnostics accuracies in the fault cases when compared with the standard measurements. These findings proved that proper measurement selection for better GPA diagnostic analysis can be achieved by using the proposed analytical methods. Several engine sensor enhancements are also discussed to accommodate the unique sensor requirements for health diagnostics using GPA.

Gas path analysis (GPA) is a powerful tool to predict gas turbine degradations based on measurement parameters of gas turbine engines. Accordingly, prudent measurement selections are crucial to ensure accurate GPA predictions. This paper is intended to investigate the influence of measurement parameter selection towards the effectiveness of GPA algorithm. An analytical methodology for measurement selection, combined with measurement subset concept, is developed to properly select measurements for multiple component fault diagnosis. The effectiveness of GPA using the measurement sets selected with the introduced measurement selection method are then compared to the results of using standard measurements installed on existing gas turbine engines. A case study applying the new measurement selection method to GPA diagnostic analysis is demonstrated on a 3-shaft aero-derivative industrial gas turbine model based on similar unit installed onboard an offshore platform operated by PETRONAS. The engine is modeled and simulated using PYTHIA, a gas turbine performance and diagnostics analysis tool developed by Cranfield University. To validate the findings, non-linear GPA prediction errors are evaluated in various cases of single and multi components faults. As a result, the selected measurements have successfully produced much superior diagnostics accuracies in the fault cases when compared to the standard measurements. These findings proved that proper measurement selection for better GPA diagnostic analysis can be achieved by using the proposed analytical methods. Several engine sensor enhancements are also discussed to accommodate the unique sensor requirements for health diagnostics using GPA.