A Deep Supervised Learning Framework Based on Kernel Partial Least Squares for Industrial Soft Sensing

Yongxuan Chen

doi:10.1109/TII.2022.3182023

A Deep Supervised Learning Framework Based on Kernel Partial Least Squares for Industrial Soft Sensing

2023-03-01

13

PRE

AI

Y

Yongxuan Chen

邓晓刚 (Xiaogang Deng) *

DOI：10.1109/TII.2022.3182023

Original

Original request for help

Save

Abstract

En 中文

Kernel partial least squares (KPLS) is a widely used soft sensor modeling method for nonlinear industrial processes. However, the traditional KPLS is considered as the shallow learning machine and may not capture the vital information hidden among data. In order to exploit the intrinsic data feature information, in this article, we propose a deep supervised learning framework based on KPLS, which is referred to as deep KPLS (DeKPLS). First, inspired by the deep learning mechanism, a hierarchical feature extraction framework based on KPLS is proposed, where the KPLS is served as the basic feature extraction module. Then, a layer-wise feedforward training strategy is designed for the determination of model architecture. Finally, two actual industrial processes are utilized to demonstrate the effectiveness of the proposed DeKPLS.

Keywords:

Soft sensors

Kernel

Feature extraction

Deep learning

Training

Data models

Testing

kernel partial least squares (KPLS)

quality prediction

soft sensor

supervised learning

A Deep Supervised Learning Framework Based on Kernel Partial Least Squares for Industrial Soft Sensing

Abstract

Journal

Organization