Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis
Autor: | Geng Jia, Song Zhiping, Jian Wang, Xuefeng Chen, Li Ming, Liu Jinxin, Liao Zengbu |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Computer science Physical system Aerospace Engineering 02 engineering and technology Fault (power engineering) 01 natural sciences Convolutional neural network 010305 fluids & plasmas Reliability engineering Consistency (database systems) 020901 industrial engineering & automation 0103 physical sciences Path (graph theory) Benchmark (computing) Sensitivity (control systems) Representation (mathematics) |
Zdroj: | Aerospace Science and Technology. 113:106724 |
ISSN: | 1270-9638 |
DOI: | 10.1016/j.ast.2021.106724 |
Popis: | Gas-path diagnosis is of great efficiency and economic benefit to gas turbines, whose algorithms are generally developed and tested by simulation. However, the existing simulation methods take insufficient consideration of a battery of uncertainties compared with the physical system. This shortcoming results in the poor performance of well-trained algorithms in the real system. A systematic representation scheme that covers all major uncertainties is urgently needed to narrow the gap between simulation and reality. This paper shows a representation scheme comprised of all major uncertainties. Various uncertainty ingredients are considered to fit the real system. The different impacts of uncertainties are monitored via a benchmark gas-path diagnosis method based on convolutional neural networks. Simulation results show the feasibility of uncertainty impact monitoring through a benchmark diagnosis method and verify the consistency between the proposed scheme and the reality. The fatal impact of the uncertainty with a slow frequency is discovered. And the evident sensitivity of the fault diagnosis to performance deterioration is identified in the end. The proposed representation scheme provides a platform where gas-path diagnosis algorithms can be compared under the unified and realistic benchmark. |
Databáze: | OpenAIRE |
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