Data-Driven Model Selection Study for Long-Term Performance Deterioration of Gas Turbines

Yuan Liu, A. Banerjee, Houman Hanachi, Amar Kumar
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引用次数: 1

Abstract

Performance of gas turbine engine (GTE) deteriorates with structural aging. The availability of operating data from GTE and capability to perform data analysis, provides an opportunity to identify long-term performance deterioration and relate to more difficult to detect structural degradation. In this work, performance analysis of a low power rating and partially loaded industrial GTE was carried out by using a model-free data analytic approach. A performance index (ratio of power generation to fuel consumption) is proposed as the metrics for monitoring the engine performance, and monitor the long-term degradation symptom. A comparative model selection study has been conducted among three multivariable models to select the best model describing long-term performance deterioration of the GTE.
燃气轮机长期性能退化的数据驱动模型选择研究
燃气涡轮发动机的性能随着结构老化而恶化。GTE的运行数据的可用性和数据分析能力,为识别长期性能恶化提供了机会,并与更难以检测到的结构退化相关。在这项工作中,使用无模型数据分析方法对低额定功率和部分负载的工业GTE进行了性能分析。提出了一种性能指标(发电/油耗比)作为监测发动机性能的指标,并对长期劣化症状进行监测。在三个多变量模型中进行了模型选择比较研究,以选择描述GTE长期绩效恶化的最佳模型。
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