Process shift of multimode process involving data distribution and dynamic relation makes traditional transfer learning methods be intractable and even result in negative transfer. To tackle this issue, this article proposes a novel self-tuning transfer dynamic modeling method for quality prediction of multimode processes. First, in order to capture domain-invariant spatiotemporal (DIST) features, a transfer dynamic convolution autoencoder (TDCAE) with a feature decomposition structure is established. Meanwhile, a first-order vector autoregressive constraint is embedded to extract consistent inner dynamics for DIST features. Then, a shared regression network is established to extract the relations with quality variables. Furthermore, by making full use of private spatiotemporal information from target labeled samples in response to the process shift, the self-tuning TDCAE (STDCAE) aided by a fine-tuning strategy is established for online compensation. Finally, the efficacy of the proposed TDCAE and STDCAE is demonstrated by a comprehensive study of a three-phase flow facility process.

中文翻译：

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用于带移位的多模过程质量预测的自调节传递动态卷积自动编码器


涉及数据分布和动态关系的多模式过程的过程转移使得传统的迁移学习方法变得棘手，甚至导致负迁移。为了解决这个问题，本文提出了一种新颖的自调节传递动态建模方法，用于多模过程的质量预测。首先，为了捕获域不变时空（DIST）特征，建立了具有特征分解结构的传输动态卷积自动编码器（TDCAE）。同时，嵌入一阶向量自回归约束以提取 DIST 特征的一致内部动态。然后，建立共享回归网络来提取与质量变量的关系。此外，通过充分利用目标标记样本的私有时空信息来响应过程转变，建立了在微调策略辅助下的自调整TDCAE（STDCAE）用于在线补偿。最后，通过对三相流设施过程的综合研究证明了所提出的 TDCAE 和 STDCAE 的有效性。