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Towards generalizable fault diagnosis:Learning invariant features across varying fault severities 认领 引用
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作者 Yiming Zhang Hongbo Shi +1 位作者 Bing Song Yang Tao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第4期23-36,共14页
Ensuring the generalizability of fault diagnosis models is critical for maintaining the long-term safety of industrial systems operating under diverse conditions.This study presents a novel method,termed the Generaliz... Ensuring the generalizability of fault diagnosis models is critical for maintaining the long-term safety of industrial systems operating under diverse conditions.This study presents a novel method,termed the Generalizable Class-Consistent Network(GCCNet),designed to enhance diagnostic robustness under previously unseen operating conditions.Speciffiifically,GCCNet incorporates a mutual information based feature disentanglement mechanism to extract task-relevant representations.To further promote feature invariance,auxiliary samples are constructed using same-class fault data under different excitation intensities,and a class-consistency regularization is applied during training to enforce consistent predictions.This guides the network to purify task-relevant features into transferable and robust representations.Extensive experiments conducted on the Tennessee Eastman process and industrial dataset validate the effectiveness and generalization ability of the proposed method. 展开更多
关键词 Fault diagnosis Domain generalization Class-consistent learning
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