Research on gas-path fault-diagnosis method of marine gas turbine based on exergy loss and probabilistic neural network

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.