Metallurgical Failure Investigation of Combustion Chamber Leakage in a Heavy-duty Gas Turbine Engine

In this contribution, a case study is presented describing the failure of a combustion chamber assembly in a non-OEM (Original Equipment Manufacturer) gas turbine engine used for power generation. It showed how even advanced fabrication methods, such as Electron Beam (EB) welding, could trigger fatigue fracture, even if there are no material defects, no weld imperfections, no fabrication flaws, and even if everything is within specified limits. As is so often the case in component failures, the fact that failures occur anyway, despite the absence of out-of-spec material properties, and even though there were no fabrication flaws, is attributable to the design; which is often not sturdy enough to withstand unexpected dynamic loading.

Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches

It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.

Metallurgical Failure Investigation of Fractured Dog Bone Seal Retainer Ring Fillet Welds in the Turbine Exhaust Casing of a Heavy-duty Gas Turbine Engine

Short fillet welds used to fasten a large retainer ring to so-called dog bone seals failed in the turbine exhaust casing of a non-OEM heavy-duty gas turbine engine used for power generation. The subject fillet welds fractured due to high cycle fatigue loading. Neither weld imperfections nor any other material defects were found that could have contributed to the failure. It was concluded that an unfavorable design, specifying very short fillet welds for fastening the dog bone seal segments to the retainer ring, was the root cause of failure. In a purely static loading situation, this design would probably not have failed. However, in a dynamic loading scenario as is the case in any gas turbine engine exhaust, such a design is simply not sturdy enough.