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Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis

机译：燃气轮机天然气道诊断的不确定性：表示和影响分析

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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. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：气体路径诊断对燃气轮机具有很大的效率和经济效益，其算法通常通过模拟开发和测试。然而，与物理系统相比，现有的模拟方法不足以考虑电池的不确定性。这种缺点导致了真实系统中训练有素的算法性能不佳。迫切需要涵盖所有主要不确定性的系统代表方案，以缩小模拟与现实之间的差距。本文显示了由所有主要不确定性组成的代表方案。认为各种不确定性成分被认为适合真实系统。基于卷积神经网络的基准天然气路径诊断方法监测不确定性的不同影响。仿真结果表明，通过基准诊断方法的不确定性影响监测的可行性，并验证了拟议方案与现实之间的一致性。发现了不确定性与慢频率的致命影响。最终鉴定了故障诊断对性能恶化的明显敏感性。所提出的代表方案提供了一个平台，可以在统一和现实的基准下比较天然气路径诊断算法。 （c）2021 Elsevier Masson SAS。版权所有。

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