General Electric Company

Lorenzo Escriche

Charles Larry Abernathy

Daniel John Maggard

Anurag Agarwal

Rajesh Alla

Frank Gruber

A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

Contamination accumulation modeling

A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.

Growing community of inventors

Hamilton, OH, United States of America

Lorenzo Escriche