On-line detection of porosity change of high temperature blade coating for gas turbine

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.