一种新的可移植软测量设计方法及其应用
A Novel Method For Designing Transferable Soft Sensors And Its Application
摘要：
本文提出了一种新的方法来设计可转移的软测量。软测量是数据驱动方法在电厂状态监测中的重要应用之一。虽然硬传感器可以很容易地用于各种工厂，但软传感器仅限于它们所设计的特定工厂，不能用于新工厂，甚至不能用于同一工厂的一些新工作条件。在本文中，提出了一种解决方案，在数据驱动的状态监测系统的这一潜在的障碍。数据驱动方法的缺点在于，构建模型所依据的数据的分布可能与模型将被应用到的数据的分布不同。这最终导致模型精度的下降。提出了一种新的基于迁移学习（TL）的回归方法，称为领域对抗神经网络回归（DANN-R），并将其用于设计可迁移软测量。我们使用从工业电厂的SCADA系统收集的数据，全面调查所提出的方法的功能。结果表明，所提出的可转移软测量可以成功地适应新的植物。
英文摘要：
In this paper, a new approach is proposed for designing transferable soft sensors. Soft sensing is one of the significant applications of data-driven methods in the condition monitoring of plants. While hard sensors can be easily used in various plants, soft sensors are confined to the specific plant they are designed for and cannot be used in a new plant or even used in some new working conditions in the same plant. In this paper, a solution is proposed for this underlying obstacle in data-driven condition monitoring systems. Data-driven methods suffer from the fact that the distribution of the data by which the models are constructed may not be the same as the distribution of the data to which the model will be applied. This ultimately leads to the decline of models accuracy. We proposed a new transfer learning (TL) based regression method, called Domain Adversarial Neural Network Regression (DANN-R), and employed it for designing transferable soft sensors. We used data collected from the SCADA system of an industrial power plant to comprehensively investigate the functionality of the proposed method. The result reveals that the proposed transferable soft sensor can successfully adapt to new plants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
