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

Energies

卷:13

Jiao Liu¹  Zhenhua Long²  Jinfu Liu²  Yujia Ma²  Daren Yu²  Mingliang Bai² 

[1] AVIC Shenyang Aircraft Design & Research Institute, Shenyang 110000, China;

[2] School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;

关键词: gas turbine;    early fault detection;    swirl effect;    quantum particle swarm optimization;   

DOI  :  10.3390/en13225950

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown