Zhao Zhijun, Yan Gaowei, Ren Mifeng, Cheng Lan, Li Rong, Pang Yusong
College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China; Shanxi Research Institute of Huairou Laboratory, Taiyuan, 030032, Shanxi, China.
ISA Trans. 2024 Oct;153:262-275. doi: 10.1016/j.isatra.2024.08.002. Epub 2024 Aug 13.
Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets.
为了解决变工况下软测量模型退化问题，并适应动态、非线性和多模态数据特征，本文提出了一种非线性动态迁移软传感器算法。该方法利用时延数据增强来捕捉动态特性，并将增强后的数据投影到潜在空间以构建非线性回归模型。在数据投影过程中引入分布对齐正则化和一阶差分正则化两个正则项，以解决数据分布差异问题。将拉普拉斯正则化纳入非线性回归模型，以确保几何结构的保留。最终的优化目标在偏最小二乘框架内制定，并使用贝叶斯优化确定超参数。通过在三个公开数据集上的实验验证了所提算法的有效性。
