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.

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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.

Gas compressor reliability is vital in oil and gas industry because of the equipment criticality which requires continuously operations. Currently, plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time-based predictive maintenance intervals as recommended by Original Equipment Manufacturer. Delayed decision on compressor maintenance intervention has caused prolonged downtime due to poor readiness of spare parts and resources. The paper discussed on development of a software-based tool which is able to assist machinery engineers to quantify performance deterioration of gas compressor and predict optimum time for maintenance activities. Maintenance history data is collected and analysed regularly and maintenance advices are subsequently produced based on the input parameters. Natural gas compressors of an oil producing offshore platform at Peninsular Malaysia are used as a case study of this project. It was found that isentropic efficiency and head decrease, but gas power increases parabolically with time for the low pressure compressors, suspected due to heavy component fouling. From these information, Compressor Performance Monitoring Program is developed which able to compute the fouling level of the compressor in terms of performance indicators deviations. The results are then being utilized to estimate future maintenance requirements based on historical data. In general, this software provides a powerful tool for gas compressor operators to realize predictive maintenance approach in their operations.

Abstract. Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM). The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.