变工况下中药制粒产线孪生迁移建模及故障诊断研究

Twin migration modeling and fault diagnosis of traditional Chinese medicine granulation production line under variable working conditions

  • 摘要: 中药制药具有多品种、小批量、工况复杂多变的生产特点,针对特定场景建立的数字孪生模型缺乏工况变化的自适应能力,难以快速准确地识别制药设备故障问题。本研究提出一种变工况下中药制粒产线孪生迁移建模及故障诊断研究方法。通过分析制药导致工况发生动态变化的影响因素,搭建制药工艺孪生模型自适应迁移框架,分析判断不同品规加工设备故障状态和时变特性,引入Swin Transformer、CNN与GRU相结合的STFusionGRU模型,融合空间与时间特征,设计包括识别、训练、更新、预测的多级自适应迁移策略,在新工况下可快速适配模型并保持高性能,解决了数据稀缺、相似故障内和相异故障间、设备异构条件下的知识迁移难题,有效提升复杂工况下设备故障的预测精度。实验结果表明:在工艺参数波动超过大、产品批次切换等典型变工况场景下,故障预测准确率达到0.98,验证了方法的有效性与实用性。本研究实现了多品规、变工况制药工艺孪生模型的自适应更新,迁移后模型的故障预测误差低于0.05,研发方法可应用于其他复杂工况下设备故障精准预测,为提高数字孪生模型自适应能力提供了新思路。

     

    Abstract: Traditional Chinese medicine (TCM) pharmaceutical production is characterised by a wide variety of products, small batch sizes, and complex and variable operating conditions. Digital twin models established for specific scenarios lack the ability to adapt to changes in operating conditions, making it difficult to quickly and accurately identify equipment faults in pharmaceutical production. This study proposes a research method for twin migration modelling and fault diagnosis of traditional Chinese medicine granulation production lines under variable operating conditions. By analysing the factors influencing dynamic changes in operating conditions caused by pharmaceutical production, a framework for the adaptive migration of pharmaceutical process twin models is established. This framework analyses and judges the fault states and time-varying characteristics of processing equipment for different product specifications. The study introduces the Swin Transformer, CNN, and GRU to fuse spatial and temporal features, and designs a multi-level adaptive migration strategy including identification, training, updating, and prediction. This approach enables rapid model adaptation and maintains high performance under new operating conditions, addressing the challenges of knowledge transfer under conditions of data scarcity, similar faults within and different faults between equipment, and heterogeneous equipment, thereby effectively improving the prediction accuracy of equipment faults under complex operating conditions. Experimental results show that in typical variable operating condition scenarios such as significant fluctuations in process parameters and product batch switching, the fault prediction accuracy reaches 0.98, validating the effectiveness and practicality of the method. This study achieved adaptive updates of multi-product, variable-condition pharmaceutical process twin models. The fault prediction error of the transferred model is below 0.05. The developed method can be applied to precise fault prediction of equipment under other complex conditions, providing

     

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