Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Yunpeng Cao, Xinran Lv, Guodong Han, Junqi Luan and Shuying Li
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Yunpeng Cao: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Xinran Lv: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Guodong Han: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Junqi Luan: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Shuying Li: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China

Energies, 2019, vol. 12, issue 24, 1-17

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.

Keywords: gas turbine; gas path; diagnosis; exergy loss; probabilistic neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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