With the complexity and intelligence of the industrial process,the identificationof faults in the actual process plays a crucial role in ensuring production safety.The traditional fault identificationstrategies have t...With the complexity and intelligence of the industrial process,the identificationof faults in the actual process plays a crucial role in ensuring production safety.The traditional fault identificationstrategies have the problem that similar characterized faults are unable to be accurately identified.Motivated by the limitations,a novel sample-optimized adaptive perceptual enhanced graph neural network(SOAPEGNN)for large-scale process fault identificationis proposed.Initially,process mechanism knowledge and process data correlation are injected into the modeling approach through graph neural networks,and the transmission of information based on the enhanced attention mechanism is introduced to describe the quantitative relationships between process variables at a fine-grainedlevel based on the adaptive perception strategy.Subsequently,to achieve better intra-class compactness and inter-class separability in feature representation,our designed sample-optimized feature processing strategy(SOFPS)is applied.Furthermore,to enhance the robustness and generalization capability of the model during training,a label smoothing regularization(LSR)strategy is incorporated.This approach effectively mitigates the risk of overfittingby introducing a degree of uncertainty into the label space,thereby encouraging the model to learn more discriminative and stable features.Ultimately,the efficacy and superiority of the SOAP-EGNN algorithm are thoroughly validated through comprehensive simulation experiments conducted on the Tennessee Eastman process(TEP).展开更多
基金sponsored by the National Natural Science Foundation of China(62473154,62473155,62473156)the Natural Science Foundation of Shanghai(19ZR1473200)+1 种基金Shanghai Oriental Talents Program Youth Project(QNKJ2024034)Shanghai Chenguang Project(21CGA37).
文摘With the complexity and intelligence of the industrial process,the identificationof faults in the actual process plays a crucial role in ensuring production safety.The traditional fault identificationstrategies have the problem that similar characterized faults are unable to be accurately identified.Motivated by the limitations,a novel sample-optimized adaptive perceptual enhanced graph neural network(SOAPEGNN)for large-scale process fault identificationis proposed.Initially,process mechanism knowledge and process data correlation are injected into the modeling approach through graph neural networks,and the transmission of information based on the enhanced attention mechanism is introduced to describe the quantitative relationships between process variables at a fine-grainedlevel based on the adaptive perception strategy.Subsequently,to achieve better intra-class compactness and inter-class separability in feature representation,our designed sample-optimized feature processing strategy(SOFPS)is applied.Furthermore,to enhance the robustness and generalization capability of the model during training,a label smoothing regularization(LSR)strategy is incorporated.This approach effectively mitigates the risk of overfittingby introducing a degree of uncertainty into the label space,thereby encouraging the model to learn more discriminative and stable features.Ultimately,the efficacy and superiority of the SOAP-EGNN algorithm are thoroughly validated through comprehensive simulation experiments conducted on the Tennessee Eastman process(TEP).
