Monitoring algorithms analyzing measured gas path variables provide invaluable insight into gas turbine operating health. Some useful information about a gas turbine and its measurement system can be obtained from a direct analysis of raw measurements. To draw more comprehensive diagnostic information, deviations are usually calculated as discrepancies between the measured and baseline values of monitored variables. The deviations can serve as good indicators of different engine degradation mechanisms. However, there are many negative factors that tend to mask degradation effects. For a long period of time we have analyzed quality of gas path measurement data and a deviation accuracy problem of a gas turbine power plant driving a natural gas pipeline compressor. Possible error sources were examined and some methods were proposed to improve the accuracy of deviation calculations. This paper looks at maintenance data of another object, the General Electric LM2500 gas turbine used as a drive of an electric generator. The data cover prolonged periods of axial compressor fouling with washings between them, and provide valuable information for a deviation examination. In order to reduce deviation errors, the paper considers different cases of the abnormal functioning of the sensors and baseline model inadequacy and proposes measures to avoid them. As a result of these and previous efforts, the deviations have become good fouling indicators. They are capable to quantify the increase of exhaust gas temperature (EGT) and, consequently, to improve planning axial compressor washings.
Simulation metamodeling in the presence of model inadequacy
