Aeroderivative gas turbines are used all over the world for different applications
as 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 and
Gas or ship propulsion. In Western Europe they are widely used in CHP small
and medium applications thanks to their maintainability and efficiency. Reliability,
Availability and Performance are key parameters when considering plant
operation and maintenance. The accurate diagnose of Performance is
fundamental for the plant economics and maintenance planning. There has been
a lot of work around units like the LM2500® , a gas generator with an
aerodynamically coupled gas turbine, but nothing has been found by the author
for the LM6000® .
Water wash, both on line or off line, is an important maintenance practice
impacting Reliability, Availability and Performance. This Thesis aims to select and
apply a suitable diagnostic technique to help establishing the schedule for off line
water wash on a specific model of this engine type. After a revision of Diagnostic
Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool.
There was no WebEngine model available of the unit under study so the first step
of setting the tool has been creating it. The last step has been testing of ANN as
a suitable diagnostic tool. Several have been configured, trained and tested and
one has been chosen based on its slightly better response. Finally, conclusions
are discussed and recommendations for further work laid out
