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)ga...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 and temperature anomalies in the gas turbine system can be identified by using the method,which has an accuracy of 60%–80%.展开更多
基金the China Datang Group Corporation of Science and Technology Project Plans(DT/KJ/2013-42)。
文摘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 and temperature anomalies in the gas turbine system can be identified by using the method,which has an accuracy of 60%–80%.
