基于迁移子空间学习的偏最小二乘回归软测量方法

针对流程工业中工况改变易导致当前样本与历史样本分布失配,传统软测量模型失准的问题,考虑工业数据时序性、动态性以及存在过程漂移等特性对建模的影响,提出一种基于迁移子空间学习的偏最小二乘回归软测量方法.首先,回归框架采用非线性迭代偏最小二乘方法,对其求解映射向量的目标函数施加基于子空间重构的域适应正则项,映射过程中保证当前工况中每个样本能够被历史工况样本线性重构.在此基础上对重构矩阵施加低秩稀疏约束,保持数据结构的同时使重构矩阵具备块状结构以应对过程漂移特性.将所提出方法在1个数值案例和3个不同的多工况数据集中进行实验,并与现有域适应回归方法进行对比分析.实验表明,所提出方法能够有效提高模型在跨工况条件下的预测精度,减少工况间数据分布差异对模型性能的影响.

Partial least squares regression soft sensor method based on transfer subspace learning

Aiming at the problem that changes in working conditions in the process industry can easily lead to mismatches between the current sample and the historical sample distribution, and the inaccuracy of traditional soft-sensing model, considering the impact of industrial data''s time series, dynamics and process drift on modeling, this paper proposes a partial least squares regression soft sensor method based on transfer subspace learning. First, the regression framework adopts the nonlinear iterative partial least squares method and applies a domain adaptation regular term based on subspace reconstruction to the objective function of solving the mapping vector. During the shooting process, it is guaranteed that each sample in the current working condition can be linearly reconstructed by the historical working condition sample. On this basis, a low-rank and sparse representation is applied to the reconstruction matrix, while maintaining the data structure, the reconstruction matrix has a block structure to deal with process drift characteristics. The proposed method is tested on a numerical case and three different multi-condition data sets and compared with the existing domain adaptive regression methods. Experiments have demonstrated that this method can effectively improve the prediction accuracy of the model under cross-conditions, and reduce the impact of the difference in data distribution between conditions on the model performance.