Fault Warning of Gas Turbine Combustor based on CNN-LSTM
In order to solve the fault problem of gas turbine combustor,a combustor fault early warning method based on convolutional neural network(CNN)and long short-term memory(LSTM)network was proposed combining with the advantage of deep learning.First,a prediction model of the combustor was constructed based on the normal historical operation data.Then,the characteristic parameters were input into the early warning model to obtain the predicted values.The deviation between the predicted and ac-tual values could reflect whether the internal work of the combustor was normal or not,and considering the nonstationary and nonlinear characteristics of the model prediction results,the sliding window method was introduced to determine the fault warning threshold.Finally,whether a fault occurs was judged ac-cording to the determined warning threshold.The above model was validated on a gas-steam combined cy-cle generator unit simulation platform.The simulation results show that the model has higher accuracy than the LSTM prediction model,and can detect the signs of failure in time and make effective early warning of the combustor failure.
万方数据
维普
