Gas turbine sensor validation through classification with artificial neural networks

Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing sensors can force a plant or component into non-optimal operation, cause complete shut-down of operation or in the worst case result in damage to components. Given their importance, sensors need regular calibration and maintenance, a time-consuming and therefore costly process. In this paper a method is presented for evaluating sensor accuracy which aims to minimize the need for calibration and at the same time avoid shut-downs due to sensor faults etc. The proposed method is based on training artificial neural networks as classifiers to recognize sensor drifts. The method is evaluated on two types of gas turbines, i.e., one single-shaft and one twin-shaft machine. The results show the method is capable of early detection of sensor drifts for both types of machines as well as accurate production of soft measurements. The findings suggest that the use of artificial neural networks for sensor validation could contribute to more cost-effective maintenance as well as to increased availability and reliability of power plants.

► Neural networks trained to detect failing sensors through classification.
► Normal operational data for model development.
► Generic applicability is verified by evaluation of two different gas turbine types.