Reactive distillation integrates reaction and distillation to achieve process intensification. And the increasing process complexity urgently requires fault diagnosis system to ensure its safe and efficient operation. However, establishing fault diagnosis model is facing intractable challenges, e.g. scarce labeled data, operating mode changeover due to product proportion variation. To tackle absent labeled data problem, pseudo-labeled database is established by integrating data mining, density-based spatial clustering of applications with noise algorithm and process knowledge, based on which unsupervised deep learning is developed. Besides, to make intelligent fault diagnosis system adaptive for multimode operation changeover, transfer learning with domain adaptation strategy is adopted, which could mitigate different data distributions in various operating states and transfer knowledge learned from source domain to target domain. Taking carbonate ester process with reactive distillation as benchmark, the performance and superiority of unsupervised learning and transfer learning is verified and demonstrated in solving the corresponding puzzle.

中文翻译：

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基于无监督和迁移学习的高纯碳酸盐工艺自适应故障诊断


反应蒸馏将反应和蒸馏结合起来，实现工艺强化。而日益增加的工艺复杂性迫切需要故障诊断系统来保证其安全高效运行。然而，建立故障诊断模型面临着一些棘手的挑战，例如标记数据稀缺、产品比例变化导致的运行模式转换等。为了解决标记数据缺失的问题，将数据挖掘、基于密度的应用空间聚类与噪声算法和过程知识相结合，建立伪标记数据库，并在此基础上发展无监督深度学习。此外，为了使智能故障诊断系统适应多模式运行转换，采用了域适应策略的迁移学习，可以减轻各种运行状态下的不同数据分布，并将从源域学到的知识迁移到目标域。以反应精馏碳酸酯工艺为基准，验证和展示了无监督学习和迁移学习在解决相应难题时的性能和优越性。