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 issue 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 and new working conditions.

在本文中，提出了一种设计可转移软传感器的新方法。软传感是数据驱动方法在植物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某些新工作条件。在本文中，针对数据驱动状态监测系统中的这一潜在障碍提出了一种解决方案。数据驱动方法存在以下事实：构建模型所依据的数据分布可能与将应用模型的数据分布不同。这个问题最终会导致模型准确率的下降。我们提出了一种新的基于迁移学习 (TL) 的回归方法，称为域对抗性神经网络回归 (DANN-R)，并将其用于设计可迁移的软传感器。我们使用从工业发电厂的 SCADA 系统收集的数据来全面调查所提出方法的功能。结果表明，所提出的可转移软传感器可以成功地适应新工厂和新工作条件。