Abstract
In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.
Highlights
A marine gas turbine operates under hostile ocean environments
This paper proposes a fault-diagnosis method based on exergy loss and a probabilistic neural network for a three-shaft marine gas turbine
The operation of a gas turbine is accompanied by exergy flow among components and the environment,and andthe theprocess processof of exergy exergy flow flow will will produce produce irreversible irreversible loss, loss, which which we call exergy loss
Summary
A marine gas turbine operates under hostile ocean environments. The air contains salt-ingested particles, which will have an impact on the gas-path components such as the compressor, combustion chamber, and turbine, and can lead to fouling, erosion, and corrosion [1,2]. Physical failure can be reflected by changes in efficiency and flow of the components, and, in turn, causes changes in gas-path thermal parameters of a gas turbine, such as pressure, temperature, rotational speed, and fuel flow rate Such relationships were described by Urban who proposes the linear gas-path analysis method [8]. This paper proposes a fault-diagnosis method based on exergy loss and a probabilistic neural network for a three-shaft marine gas turbine.
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