Abstract The health status of high-temperature blades is one of the key factors that affect the operation of the gas turbine. Since turbine blades usually work in high temperature and high pressure environments, the thermal insulation performance of Thermal barrier coating (TBC) is the main factor to determine the service life of high-temperature blades. The change of porosity of TBC has a great influence on its thermal insulation performance. In order to establish an on-line monitoring system for thermal insulation performance of high temperature blade TBC, it is necessary to obtain the microstructure evolution law of TBC in real time. In this work, a coupling algorithm based on Gray gradient space histogram entropy (GGSHE) and Sparse representation-based classifier (SRC) was developed. The investigation of this Non-destructive testing (NDT) method is important to evaluate the variation of the porosity of TBC with the service time by analyzing the image of the surface temperature distribution of the coating. The effectiveness of GGSHE is trained and verified by using the numerical data, and compared with other algorithms. The results show that the GGSHE has the best detection performance: the Average error rate (AER) of GGSHE is 3.23% under the constant working condition, and 4.18% under the changing working condition, which are both the best results. Moreover, when the microstructure of coating changes or the temperature legend of infrared thermal imaging becomes larger, GGSHE can still extract the feature of pores effectively and can achieve high detection accuracy. The detection time of GGSHE is 4.04 s, meeting the time requirements of on-line detection of high temperature blades.

中文翻译：

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燃气轮机高温叶片涂层孔隙率变化在线检测

摘要 高温叶片的健康状况是影响燃气轮机运行的关键因素之一。由于涡轮叶片通常工作在高温高压环境中，热障涂层（TBC）的隔热性能是决定高温叶片使用寿命的主要因素。TBC孔隙率的变化对其保温性能影响很大。为了建立高温叶片TBC绝热性能在线监测系统，需要实时获取TBC的微观结构演化规律。在这项工作中，开发了一种基于灰度梯度空间直方图熵（GGSHE）和基于稀疏表示的分类器（SRC）的耦合算法。这种无损检测 (NDT) 方法的研究对于通过分析涂层表面温度分布的图像来评估 TBC 孔隙率随使用时间的变化非常重要。利用数值数据对GGSHE的有效性进行了训练和验证，并与其他算法进行了比较。结果表明，GGSHE的检测性能最好：GGSHE的平均错误率（AER）在恒定工作条件下为3.23%，在变化工作条件下为4.18%，两者都是最好的结果。此外，当涂层微观结构发生变化或红外热成像温度图例变大时，GGSHE仍能有效提取孔隙特征，实现较高的检测精度。GGSHE的检测时间为4.04 s，