Research Papers: Gas Turbines: Cycle Innovations

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

[+] Author and Article Information
Mohd Shahrizal Jasmani

Maintenance Engineering Department, PETRONAS Carigali Sdn. Bhd., 24300 Kerteh, Terengganu, Malaysia

Yi-Guang Li

Department of Power and Propulsion, Cranfield University, Cranfield, Bedfordshire, MK43 0AL UK

Zaharudin Ariffin

Group Technology Solutions, PETRONAS, 50090 Kuala Lumpur, Malaysia

J. Eng. Gas Turbines Power 133(11), 111701 (May 13, 2011) (10 pages) doi:10.1115/1.4002348 History: Received April 28, 2010; Revised August 05, 2010; Published May 13, 2011; Online May 13, 2011

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.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Measurement selection methodology

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Figure 2

Venn diagram for measurement subset concept

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Figure 3

Gas turbine components and existing sensor layout

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Figure 4

PYHTIA model and station numberings

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Figure 5

Component degradation test cases

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Figure 6

Sensitivity analysis bar chart

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Figure 7

Correlations analysis bar chart

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Figure 8

Measurement subset diagram

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Figure 9

NLGPA RMS errors (single component fault cases)

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Figure 10

NLGPA RMS errors (two component fault cases)

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Figure 11

NLGPA RMS errors (three or more component fault cases)



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