基于SlowFast-Transformer的化工过程故障检测与风险预警OA

To address the high dimensionality,nonlinearity,and strong temporal correlation of chemical process data,a multi-scale spatiotemporal fusion fault diagnosis method based on SlowFast network and Transformer is proposed.The method is designed with a dual-channel feature extraction architecture:the Slow pathway employs temporally down-sampled convolutions to capture macro-level process features,while the Fast pathway retains high temporal resolution convolutions to extract local dynamic characteristics.By integrating the self-attention mechanism of Transformer,long-range dependencies are effectively modeled.A sliding window is used to construct spatiotemporal samples,ultimately achieving accurate fault classification and real-time monitoring.Experimental results on the TE process dataset show that the proposed model exhibits excellent diagnostic performance on the test set with 4 layers of Transformer blocks stacked.Compared with existing models such as DCNN(88.20％),LSTM(95.37％),CNN-LSTM(96.64％),and DCRNN(91.70％),the proposed approach significantly improves fault diagnosis accuracy(99.14％).By introducing the SlowFast architecture from the video analysis domain into chemical process time-series modeling and combining it with the global perception capability of Transformer,the classification effectiveness is substantially enhanced.Furthermore,a novel early-warning evaluation metric is proposed,and the relationship between the diagnostic performance and interpretability of SlowFast is discussed based on experimental results,providing a new technical approach and theoretical reference for fault diagnosis in complex industrial processes.