Research on fault diagnosis of marine steam turbine units during variable load process based on TREE-LSTM algorithm
In response to the difficulties in capturing the coupling parameter time series characteristics and the interfer-ence of normal parameter changes in fault diagnosis during the variable load process of marine steam turbine units,the TREE-LSTM neural network model is introduced to achieve dynamic data classification of complex nonlinear systems.Firstly,establish a simulation model for a certain marine steam turbine unit,analyze the fault mechanism,and conduct fault simulation;subsequently,perform data preprocessing and feature engineering;finally,a TREE-LSTM model was built for training and fault diagnosis,and compared with models such as SVM and LSTM.The TREE-LSTM model has a fault dia-gnosis accuracy of 98.7％for the variable load process of marine steam turbine units,with the highest accuracy.It is ulti-mately believed that due to the introduction of time series and complex neural network topology,TREE-LSTM performs bet-ter in dealing with dynamic data classification problems in nonlinear systems.
steam turbine unitdynamic simulationfault diagnosistree long short-term memory network(TREE-LSTM)
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