Measurement Selections for Multicomponent Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept

Abstract

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 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 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, nonlinear GPA prediction errors are evaluated in various cases of single and multicomponents 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.