Deposition and congestion of foulants in the compressor section of gas turbine engines (GTE) degrades the compressor and leads to performance deterioration of the GTE at the system level. Compressor fouling may develop over a short time, but it is recoverable by washing and cleaning. Reliable prediction of the fouling as a function of time is helpful for planning compressor wash services. In this work, the fouling state is parametrized as the relative change of the ratio of the compressor mass flow and efficiency against ideal conditions. A regression-based prognostic model is developed to predict the fouling state as a function of time. In the next step, an adaptive neuro-fuzzy inference system (ANFIS) is developed that considers the rate of humidity condensation at the inlet of the compressor for the prognostic model. The performance of the developed models is evaluated with recorded operating data from a GTE in a power plant. The study shows that enhancement of the prognostic model may be accomplished by taking into account the effects of humidity on the rate of fouling and results in an improvement in the prognostic accuracy.

Additional Metadata
Keywords ANFIS, compressor fouling, degradation prediction, gas turbine prognostics, performance deterioration
Persistent URL dx.doi.org/10.1109/ICPHM.2017.7998307
Conference 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017
Citation
Hanachi, H. (Houman), Mechefske, C. (Christopher), Liu, J, Banerjee, A. (Avisekh), & Chen, Y. (Ying). (2017). Enhancement of prognostic models for short-term degradation of gas turbines. In 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017 (pp. 66–69). doi:10.1109/ICPHM.2017.7998307