基于对比记忆自编码器的烟机运行状态监控方法OA

[Objective]This study proposes a contrastive memory autoencoder-based method for detecting the production operating status of cigarette-making machines to improve cigarette production quality.[Methods]Using empty-end data from the Hangzhou cigarette factory as an example,the model was trained on raw key indicators of cigarette rods,including weight,circumference,draw resistance,and empty ends.A contrastive learning framework was used to pretrain an encoder network capable of capturing cigarette-quality features.By amplifying the discrepancy between anomalous and normal data,the method detects production anomalies.In addition,an anomaly ratio and gating parameter were introduced to optimize memory-vector updating and improve model performance.[Results]The model achieved an F1 score,recall,and AUC of 0.9801,1.0000,and 0.9938,respectively,outperforming the current comparison methods and confirming its effectiveness and superiority.[Conclusion]The proposed method does not rely on cigarette-machine fault data.It can accurately and reliably determine cigarette-machine operating status using only normal operating data,indicating substantial potential for practical application.