Fault warning technology for compressor unit based on integration of Attention and GRU
The existing natural gas compressor unit generally adopts maintenance after a fault and regular maintenance,which will cause disrepair or over-repair of the compressor unit.To solve this problem,a fault warning technology for compressor units based on the integration of Attention and gate recurrent unit(GRU)was proposed.The random forest(RF)algorithm was first used to screen parameters affecting the corresponding faults of the compressor unit,and the formed data set was input into the GRU model for training and prediction.An Attention mechanism was established in the hidden layer and the fully connected layer to assign weight to the key data in a single time step.Finally,based on the residual mean of the actual field value and the predicted value of the model,the risk level at different time was determined by calculating the membership degree of the trigonometric function.The results show that the Attention mechanism has the greatest influence on the accuracy of the model,followed by the GRU model.The risk membership degree can realize the visualization of the risk information of the predicted data.The early warning time of the pressure difference fault of the air intake filter can be 133 h in advance,and that of the surge fault of the compressor unit can be 204 min in advance.The research results can provide a practical reference for big data analysis of process control systems and early fault warnings of compressor units.
万方数据
维普
