Research on Gas Turbine Fault Warning Method Based on Improved Convolutional Adversarial Network
To ensure the long-term normal operation of gas turbine generator sets,there is an urgent need for research on the fault warning method for gas turbine generator sets.A method of gas turbine fault warning based on improved convolutional adversarial network is proposed.Firstly,structural improvements are made to the generator and discriminator in the original structure respectively to enhance the performance of the model and make the model directly output quantized evaluation values.Then,the loss value calculation functions of the generator and discriminator are optimized to extend the effective range.Finally,the test is carried out with an F-type gas turbine generator set as the target.The experimental results show that the method can get rid of the dependence of existing research on calibrated fault data sets,complete the quantitative evaluation conforming to the qualitative estimation for unknown categories of data,and achieve more than 98％fault warning accuracy for multiple categories of unknown data.This research contributes to the greenization and intelligence of cities.
Deep learningGreen CityGas turbineGenerator setConvolutional adversarial networkFault warning
万方数据
维普
