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  • 變工況下中藥制粒產線孿生遷移建模及故障診斷研究

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

    • 摘要: 中藥制藥具有多品種、小批量、工況復雜多變的生產特點,針對特定場景建立的數字孿生模型缺乏工況變化的自適應能力,難以快速準確地識別制藥設備故障問題。本研究提出一種變工況下中藥制粒產線孿生遷移建模及故障診斷研究方法。通過分析制藥導致工況發生動態變化的影響因素,搭建制藥工藝孿生模型自適應遷移框架,分析判斷不同品規加工設備故障狀態和時變特性,引入Swin Transformer、CNN與GRU相結合的STFusionGRU模型,融合空間與時間特征,設計包括識別、訓練、更新、預測的多級自適應遷移策略,在新工況下可快速適配模型并保持高性能,解決了數據稀缺、相似故障內和相異故障間、設備異構條件下的知識遷移難題,有效提升復雜工況下設備故障的預測精度。實驗結果表明:在工藝參數波動超過大、產品批次切換等典型變工況場景下,故障預測準確率達到0.98,驗證了方法的有效性與實用性。本研究實現了多品規、變工況制藥工藝孿生模型的自適應更新,遷移后模型的故障預測誤差低于0.05,研發方法可應用于其他復雜工況下設備故障精準預測,為提高數字孿生模型自適應能力提供了新思路。

       

      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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