Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Weiming Shao; Xuemin Tian; Ping Wang

doi:10.1016/j.cjche.2014.05.003

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Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

SOFT SENSOR | Updated：2026-01-06

- Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division
- Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division
- 中国化学工程学报（英文版） 2014年22卷第7期页码：828-836
- Affiliations：
  
  1. College of Information and Control Engineering, China University of Petroleum (Huadong),Qingdao,China,266580
  2. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Huadong),Qingdao,China,266580
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.003
  中图分类号：
- 纸质出版：2014
- Accepted：
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Weiming Shao, Xuemin Tian, Ping Wang. Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division[J]. 中国化学工程学报（英文版）, 2014,22(7):828-836.

Weiming Shao, Xuemin Tian, Ping Wang. Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division[J]. , 2014, 22(7): 828-836.
Weiming Shao, Xuemin Tian, Ping Wang. Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division[J]. 中国化学工程学报（英文版）, 2014,22(7):828-836. DOI： 10.1016/j.cjche.2014.05.003.

Weiming Shao, Xuemin Tian, Ping Wang. Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division[J]. , 2014, 22(7): 828-836. DOI： 10.1016/j.cjche.2014.05.003.

摘要

Abstract

Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper

a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation

which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test

where unique local time regions are extracted. Subsequently

a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

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