Study of the Optimisation of Measurement Sets for Gas Path Fault Diagnosis in Gas Turbines

Author:

Ogaji S. O. T.1,Singh R.1

Affiliation:

1. Cranfield University, Cranfield, UK

Abstract

The reliability of the gas path components (compressor, burners and turbines) of a gas turbine (GT) is usually high when compared with other GT systems such as fuel and control. However, their availability could be relatively low as high downtimes are normally associated with these components when subjected to forced outages. One way of improving availability is by improved maintenance practices that involve applying such approaches as condition based monitoring (CBM). Unfortunately, this cannot be achieved without the existence of a proper instrumentation set that can adequately and repeatedly track down the levels of deterioration in these components, thereby allowing for optimally scheduled maintenance. Different engine handles (operating point or parameter that is held constant with respect to other parameters) would require different instrumentation sets for proper gas path fault diagnosis. Sometimes, the instrumentation sets used makes the required diagnostic analysis impossible. Furthermore, allowing redundancy in instrumentation, unless specified with knowledge of the diagnostic technique to be used, is not only unnecessary but also cost ineffective. The central theme of this paper is to present a means of attaining an optimum instrumentation set using a non-linear gas path analysis (NLGPA) programme. Firstly, some of the common gas path faults are considered, some theoretical backing is given to the principles involved in this work, the implications of unoptimised instrumentation set as viewed from the users’ perspective is examined and finally, results presented for the NLGPA approach when applied to a two-shaft and a three-shaft industrial gas turbine. Also, we show how the engine handle can affect the choice of the instrumentation set.

Publisher

ASMEDC

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1. Model-based Performance Analysis and Health Monitoring of a Low Bypass Ratio Turbofan Engine;2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST);2022-08-16

2. Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines;Journal of the Brazilian Society of Mechanical Sciences and Engineering;2018-11-24

3. Comparison of Linear and Nonlinear Gas Turbine Performance Diagnostics;Journal of Engineering for Gas Turbines and Power;2005-01-01

4. Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine;Applied Energy;2004-08

5. A Generic Approach for Gas Turbine Adaptive Modeling;Journal of Engineering for Gas Turbines and Power;2004-03-01

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