Xian Ma1, Tingyan Lv2,*, Yingqiang Jin2, Rongmin Chen2, Dengxian Dong2, Yingtao Jia2
Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701
Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as chamber seal failure, load abnormality… More >
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