LAPSE:2019.0617
Published Article
LAPSE:2019.0617
A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T2 and SPE
Li Zeng, Wei Long, Yanyan Li
July 5, 2019
Gas turbines are widely used all over the world, in order to ensure the normal operation of gas turbines, it is necessary to monitor the condition of gas turbine and analyze the tested parameters to find the state information contained in parameters. There is a problem in gas turbine condition monitoring that how to locate the fault accurately if failure occurs. To solve the problem, this paper proposes a method to locate the fault of gas turbine components by evaluating the sensitivity of tested parameters to fault. Firstly, the tested parameters are decomposed by the kernel principal component analysis. Then construct the statistics of T2 and SPE in the principal elements space and residual space, respectively. Furthermore, the thresholds of the statistics must be calculated. The influence of tested parameters on faults is analyzed, and the degree of influence is quantified. The fault location can be realized according to the analysis results. The research results show that the proposed method can realize fault diagnosis and location accurately.
Keywords
kernel function, KPCA, SPE statistical model, T2 statistical model
Suggested Citation
Zeng L, Long W, Li Y. A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T2 and SPE. (2019). LAPSE:2019.0617
Author Affiliations
Zeng L: School of Aeronautics & Astronautics, Sichuan University, Chengdu 610065, China
Long W: School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China
Li Y: School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China
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Journal Name
Processes
Volume
7
Issue
3
Article Number
E124
Year
2019
Publication Date
2019-02-27
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr7030124, Publication Type: Journal Article
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LAPSE:2019.0617
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doi:10.3390/pr7030124
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CC BY 4.0
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Jul 5, 2019
 
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Original Submitter
Calvin Tsay
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