In order to solve the problem that the diagnostic accuracy of current gas turbine gas path faults based on data-driven needs to be improved

a gas path fault diagnosis method based on parallel optimization of dual-channel feature fusion is proposed. Firstly

a gas turbine thermodynamic model is used to construct a sample set of gas path fault data. Secondly

convolutional neural network and long and short term memory neural network are used to mine the spatial and temporal features of the data in parallel

and the optimization methods of the first layer large convolutional kernel and squeeze excitation network are introduced in the two channels respectively. Finally

the features extracted from the two channels are fused into a one-dimensional tensor and then reconstructed into the fully connected layer for gas path fault type identification. The experimental results show that

the proposed method has better recognition accuracy than the conventional machine learning and deep learning gas path fault diagnosis methods

with an average diagnosis accuracy of 98.24%

which is practical and feasible.