International Society for Air Breathing Engines (ISABE)
Abstract
The role of diagnostic systems in gas turbine operations has changed over the past years from a single support
troubleshooting maintenance to a more proactive integrated diagnostic system. This has become so, because
detecting and fixing fault(s) on one gas turbine sub-system can trigger false fault(s) indication, on other
component(s) of the gas turbine system, due to interrelationships between data obtained to monitor not only the
GT single component, but also the integrated components and sensors. Hence, there is need for integration of
gas turbine system diagnostics. The purpose of this paper is to present artificial neural network diagnostic
system (ANNDS) as an integrated gas turbine system diagnostic tool capable of quantifying gas turbine
component and sensor fault. A model based approach which consists of an engine model, and an associated
parameter estimation algorithm that predicts the difference between the real engine data and the estimated
output data is described in this paper. The ANNDS system was trained to detect, isolate and assess
component(s) and sensor fault(s) of a single spool industrial gas turbine GT-PG9171ER. The ANN model was
construed with multi-layer feed-forward back propagation network for component fault(s) and auto associative
network for sensor fault(s). The diagnostic methodology adopted was a nested network structure, trained to
handle specific objective function of detecting, isolating or quantifying faults. The data used for training, and
testing purposes were obtained from a non-linear aero-thermodynamic model using PYTHIA; a Cranfield
University in-house software. The data set analyzed in this paper represent samples of clean and faulty gas
turbine components caused by fouling (0.5% - 6% degradation) and sensor fault(s) due to bias (Β±1% - Β±7%).
The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly
trained
