Data-driven methods have shown its great latent capacity in the field of industrial process monitoring. However, the existing methods usually achieve good results under the assumption that the offline learning data and the online monitoring data are drawn from the same distribution. Unfortunately, in the industrial system, the assumption is often violated due to the harsh operating environment. Especially, with the increasing complexity and scale of industrial production, the supervisory control and data acquisition (SCADA) data of the industrial production process often collected from different machines, seasons, or operating modes. In addition, due to the cost of manual data labeling and real-time requirement of process monitoring, the offline learning data, which was used to build the model, often have abundant source-domain data and insufficient target-domain data. Consequently, these methods have bad performance on the online monitoring data collected from the target domain. In order to make full use of the knowledge from the abundant source-domain data, a transfer dictionary learning method is proposed to address the cross-domain problem in this article. The proposed method can learn an initial dictionary from the abundant source-domain data, and then, the final dictionary is updated by incorporating the feature of insufficient target-domain data in a smooth subspace interpolation way. The effectiveness of the proposed method is evaluated through a numerical simulation case, a continuous stirred tank heater (CSTH) case, and a wind turbine system case, from which we can see the proposed method has a better performance compared with some state-of-the-art methods.

中文翻译：

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跨域多模过程监控和故障隔离的迁移字典学习方法

数据驱动的方法在工业过程监控领域已经显示出其巨大的潜力。然而，现有方法通常在假设离线学习数据和在线监控数据来自同一分布的情况下取得良好的效果。不幸的是，在工业系统中，由于恶劣的操作环境，该假设经常被违反。尤其是随着工业生产复杂性和规模的日益增加，工业生产过程的监控和数据采集（SCADA）数据往往从不同的机器、季节或操作模式中收集。此外，由于人工数据标注的成本和过程监控的实时性要求，用于构建模型的离线学习数据，往往源域数据丰富，目标域数据不足。因此，这些方法在从目标域收集的在线监控数据上表现不佳。为了充分利用丰富的源域数据中的知识，本文提出了一种迁移字典学习方法来解决跨域问题。该方法可以从丰富的源域数据中学习初始字典，然后通过平滑子空间插值的方式结合目标域数据不足的特征来更新最终字典。通过数值模拟案例，连续搅拌罐加热器（CSTH）案例和风力涡轮机系统案例评估所提出方法的有效性，