Yamada Nobuhito, Kaneko Hiromasa
Department of Applied Chemistry School of Science and Technology Meiji University Kawasaki Japan.
Anal Sci Adv. 2022 Jun 10;3(5-6):205-211. doi: 10.1002/ansa.202200013. eCollection 2022 Jun.
The objective of this study is to develop an adaptive software sensor technique that can predict objective process variables for a target grade in a plant while also considering information related to various other grades. We use a dataset of the target grade as the target domain and those of the other grades as source domains to perform transfer learning. Multiple models or sub-models are constructed by setting a source domain for each grade and changing the number of samples used as the source domain. Furthermore, to prevent the negative transfer, the use of a source domain is automatically judged. In this study, we constructed sub-models using the locally weighted partial least squares approach as an adaptive soft sensor technique. The values of an objective variable were predicted with ensemble learning using sub-models. The effectiveness of the proposed method was verified using a dataset measured in an actual incineration plant, and the proposed method was able to accurately predict the product quality even when the plant was operated in five grades and when a new grade was produced.
本研究的目的是开发一种自适应软件传感器技术，该技术可以预测工厂中目标等级的客观过程变量，同时还考虑与其他各种等级相关的信息。我们使用目标等级的数据集作为目标域，其他等级的数据集作为源域来进行迁移学习。通过为每个等级设置一个源域并改变用作源域的样本数量，构建多个模型或子模型。此外，为了防止负迁移，自动判断源域的使用情况。在本研究中，我们使用局部加权偏最小二乘法作为自适应软传感器技术构建子模型。使用子模型通过集成学习预测目标变量的值。使用在实际焚烧厂测量的数据集验证了所提方法的有效性，并且所提方法即使在工厂以五个等级运行以及生产新等级时也能够准确预测产品质量。
