Design of Super-X Test Facility monitoring and early warning system based on improved CNN-BiLSTM model

To issue early warning information before the failure of Super-X Test Facility, and to monitor the quality of the operation status and real-time data flow, a fusion attention mechanism BiLSTM model based on multi-scale CNN is proposed. Based on the architecture of experimental physics and industrial control system, the method reads the collected monitoring data to the monitoring platform through the data interface, and then realizes the anomaly detection and processing of the data through the steps of transformation regression analysis, time series monitoring, correlation monitoring and intelligent algorithm, which is mainly composed of four parts: data collection, anomaly identification and early warning information. A fault diagnosis method of convolutional neural network and bidirectional long short-term memory network algorithm based on attention mechanism is designed, and the attention mechanism is introduced to adjust the weight of captured information vectors to make up for the shortcomings of precocious convergence. The test results show that the system has a high degree of accuracy for the detection of abnormal data in Super-X Test Facility, which meets the actual production needs.