11th European Conference on Turbomachinery Fluid dynamics & Thermodynamics

Paper ID:

ETC2015-051

Main Topic:

OPERATIONAL EXPERIENCE

Authors

V. Panov - Siemens Industrial Turbomachinery Ltd., UK

Abstract

This study presents the method for detection and isolation of component faults and degradation modes in industrial gas turbine engine. Performance of gas turbine engines gradually deteriorate over the service life due to degradation of the gas path components such as compressor, combustor and turbines. These physical faults gradually evolve over a prolonged period of operation and lead to degradation of the performance parameters, such as efficiency and flow capacity of individual gas-path components. Performance degradation, in turn, causes changes in the measurable engine parameters, such as temperature, pressure, rotational speed, and fuel flow rate. Traditionally these component degradation modes and faults in the engine have been detected by measuring the changes in these observable parameters through appropriate usage of signal processing and pattern recognition tools. In this contribution model-based diagnostic approach has been applied, where measureable parameters have been used to estimate so-called engine health parameters, i.e. component efficiencies and flow capacities. Health parameter deviations from nominal conditions are subsequently used to obtain health indices and the best signature match is then used to identify likely component degradation modes and faults. Performance diagnostic and fault isolation process is based on multidimensional complex health vector space which contains generated health indices, i.e. component capacity and efficiency indices for different gas turbine components. Simulated gas turbine degradation modes have been diagnosed and isolated by comparing gas turbine health vector against bank of fault signatures for different gas-path components.

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