Abstract Gas turbines are widely used in natural gas-fired power generation and long-distance natural gas pipelines. The complexity and diversity of boundary conditions usually have a great influence on the measurement parameters, which makes them difficult to reflect the actual health status of gas turbines and leads to lower diagnostic accuracy. In the gas path analysis of gas turbines, traditional mechanism and data-driven methods are difficult to consider the interference of boundary conditions. A novel fault diagnostic method deriving from the spatial transformer network (STN) is proposed to restrain the influences of boundary conditions on fault diagnostic results. It can realize fault detection with higher accuracy by converting different boundary conditions to a designed one. The established STN is then combined with the typical small deviation (SD) theory and a classification network to complete the gas path analyses. The proposed two methods are verified and compared with traditional methods through simulation experiments and field data. Compared with the traditional SD method, the fault detection accuracy is increased by 2.92% in the STN-SD method, and the STN-MLP method improves the diagnostic accuracy by 5.24%. The proposed two methods also have good performance in the field data analysis and realize fast fault detection and early warning. This work provides a reliable way to detect faults subjecting to the influence of complex boundary conditions and provides reference and support for the fault diagnosis of other thermodynamic systems.

中文翻译：

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基于STN的抑制边界条件干扰的热力学分析燃气轮机故障诊断

摘要 燃气轮机广泛应用于天然气发电和长输天然气管道。边界条件的复杂性和多样性通常对测量参数影响较大，难以反映燃气轮机的实际健康状况，导致诊断精度较低。在燃气轮机的气路分析中，传统的机理和数据驱动方法难以考虑边界条件的干扰。为了抑制边界条件对故障诊断结果的影响，提出了一种基于空间变压器网络（STN）的新型故障诊断方法。通过将不同的边界条件转换为设计好的边界条件，可以实现更高精度的故障检测。然后将建立的 STN 与典型的小偏差 (SD) 理论和分类网络相结合，以完成气路分析。通过仿真实验和现场数据，对提出的两种方法进行了验证，并与传统方法进行了比较。与传统的SD方法相比，STN-SD方法的故障检测精度提高了2.92%，STN-MLP方法的诊断精度提高了5.24%。所提出的两种方法在现场数据分析中也具有良好的性能，实现了快速故障检测和预警。该工作为检测受复杂边界条件影响的故障提供了一种可靠的方法，并为其他热力系统的故障诊断提供了参考和支持。