Open Access


Cloud Based Monitoring and Diagnosis of Gas Turbine Generator Based on Unsupervised Learning

Xian Ma1, Tingyan Lv2,*, Yingqiang Jin2, Rongmin Chen2, Dengxian Dong2, Yingtao Jia2
1 China Academy of Industrial Internet, Beijing, 100041, China
2 China Datang Co. Ltd., Beijing, 100032, China
* Corresponding Author: Tingyan Lv. Email:

Energy Engineering 2021, 118(3), 691-705.

Received 09 July 2020; Accepted 09 November 2020; Issue published 22 March 2021


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%.


Gas turbine generation; machine learning; cloud computing; monitoring and diagnostics

Cite This Article

Ma, X., Lv, T., Jin, Y., Chen, R., Dong, D. et al. (2021). Cloud Based Monitoring and Diagnosis of Gas Turbine Generator Based on Unsupervised Learning. Energy Engineering, 118(3), 691–705.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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