Prediction and Anomaly Detection of Coal Mill Outlet Pressure Using a PSO-3DResNet Model

This paper improves the deep residual network, proposes 3DResNet network and carries out particle swarm optimization, constitutes the PSO-3DResNet model, and designs the coal mill fault diagnosis model based on PSO-3DResNet model.The technical parameters, common fault types and fault characteristics of the coal mill are analyzed, and the relationship between the input and output parameters of the coal mill is decomposed by the residual-based condition monitoring method.Combining the numerical simulation model of coal mill and historical operation data, the typical fault condition monitoring of coal mill is constructed.Compare the classification accuracy of each model on the working state of blast furnace wind mouth, and get the anomaly detection performance of each model.The PSO-3DResNet model is analyzed to monitor the normal operating state of the coal mill, and the model is tested using the historical current and outlet wind temperature anomaly data of the coal mill.When the coal mill is in an abnormal state, the estimated residuals of the current abnormal condition fluctuate within [-16,3] with a small range, and the weighted average residuals of the current abnormal condition index remain within [-4,1].