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.
When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) model of the marine gas turbine is established, and the exergy loss analysis under normal conditions is carried out to verify the accuracy of the model. The fault diagnosis of gas turbine gas path based on EIL is proposed, and a simulation experiment conducted on a three-shaft marine gas turbine demonstrated that the proposed approach can detect and isolate gas path fault accurately under different operating conditons and enviroments.
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