Performance based diagnostics of a twin shaft aeroderivative gas turbine: water wash scheduling

摘要

Aeroderivative gas turbines are used all over the world for different applicationsas Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others.They combine flexibility with high efficiencies, low weight and small footprint,making them attractive where power density is paramount as off shore Oil andGas or ship propulsion. In Western Europe they are widely used in CHP smalland medium applications thanks to their maintainability and efficiency. Reliability,Availability and Performance are key parameters when considering plantoperation and maintenance. The accurate diagnose of Performance isfundamental for the plant economics and maintenance planning. There has beena lot of work around units like the LM2500® , a gas generator with anaerodynamically coupled gas turbine, but nothing has been found by the authorfor the LM6000® .Water wash, both on line or off line, is an important maintenance practiceimpacting Reliability, Availability and Performance. This Thesis aims to select andapply a suitable diagnostic technique to help establishing the schedule for off linewater wash on a specific model of this engine type. After a revision of DiagnosticMethods Artificial Neural Network (ANN) has been chosen as diagnostic tool.There was no WebEngine model available of the unit under study so the first stepof setting the tool has been creating it. The last step has been testing of ANN asa suitable diagnostic tool. Several have been configured, trained and tested andone has been chosen based on its slightly better response. Finally, conclusionsare discussed and recommendations for further work laid out.