Noise-Tolerant Co-Trained Semisupervised Soft Sensor Model for Industrial Process

Soft sensors are often used to obtain variables that are difficult to directly measure in an industrial process. In this article, a co-training-based semisupervised soft sensor model is presented to address noise removal and labeled sample acquisition in an industrial process. To fully utilize unlabeled samples, a semisupervised method based on co-training is proposed to build a semisupervised model based on
$\varepsilon $
-SVR (SSCo-
$\varepsilon $
SVR) soft sensor model. At the same time, to improve the noise tolerance of the soft sensor model,
$\varepsilon $
-insensitive loss is introduced in the training process. To improve the nonlinear data estimation ability of the soft sensor model, the gradient-boost ensemble learning method is introduced and an ensemble SSCo-
$\varepsilon $
SVR (ESSCo-
$\varepsilon $
SVR) soft sensor model is established. The noise tolerance and generalization ability of the soft sensor model are discussed using numerical simulations. Furthermore, the debutanizer column data and actual data from a coking process are used to establish a soft sensor model. The results demonstrate that the method is effective and practical.