Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation

Jinfu Liu, Mingliang Bai, Zhenhua Long, Jiao Liu, Yujia Ma and Daren Yu
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Jinfu Liu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Mingliang Bai: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Zhenhua Long: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Jiao Liu: AVIC Shenyang Aircraft Design & Research Institute, Shenyang 110000, China
Yujia Ma: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Daren Yu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Energies, 2020, vol. 13, issue 22, 1-13

Abstract: Failures of the gas turbine hot components often cause catastrophic consequences. Early fault detection can detect the sign of fault occurrence at an early stage, improve availability and prevent serious incidents of the plant. Monitoring the variation of exhaust gas temperature (EGT) is an effective early fault detection method. Thus, a new gas turbine hot components early fault detection method is developed in this paper. By introducing a priori knowledge and quantum particle swarm optimization (QPSO), the exhaust gas temperature profile continuous distribution model is established with finite EGT measuring data. The method eliminates influences of operating and ambient condition changes and especially the gas swirl effect. The experiment reveals the presented method has higher fault detection sensitivity.

Keywords: gas turbine; early fault detection; swirl effect; quantum particle swarm optimization (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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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