DLNS-PCA-based fault detection for multimode batch process

FENG Liwei; ZHANG Cheng; LI Yuan; XIE Yanhong

doi:10.11949/j.issn.0438-1157.20171629

您当前的位置：

首页 >

文章列表页 >

DLNS-PCA-based fault detection for multimode batch process

更新时间：2025-12-10

- DLNS-PCA-based fault detection for multimode batch process
- CIESC Journal Issue 7, Pages: 3159-3166(2018)
- 作者机构：
  
  1. 沈阳化工大学数理系,辽宁,沈阳,110142
  2. 沈阳化工大学过程故障诊断研究中心,辽宁,沈阳,110142
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20171629
  CLC： P277
- Online First：05 July 2018，
  
  Published：05 July 2018
- 稿件说明：
移动端阅览
FENG Liwei, ZHANG Cheng, LI Yuan, et al. DLNS-PCA-based fault detection for multimode batch process[J]. CIESC Journal, 2018, (7): 3159-3166.
DOI：

FENG Liwei, ZHANG Cheng, LI Yuan, et al. DLNS-PCA-based fault detection for multimode batch process[J]. CIESC Journal, 2018, (7): 3159-3166. DOI： 10.11949/j.issn.0438-1157.20171629.

摘要

现代工业产品的生产往往需要多个生产阶段，多阶段生产过程的故障检测成为一个重要问题。多阶段过程数据具有多中心、各工序数据结构不同等特征。针对多阶段过程数据的特征，提出了基于双近邻标准化和主元分析的故障检测方法（DLNS-PCA）。首先寻找样本的双层局部近邻集；其次使用双层局部近邻集的信息标准化样本，得到标准样本；最后在标准样本集上使用主元分析方法进行故障检测。双局部近邻标准化能够将各阶段数据的中心平移到同一点，并且调整各阶段数据的离散程度，使之近似相等，从而将多阶段过程数据融合为服从单一多元高斯分布的数据。进行了青霉素发酵过程故障检测实验，实验结果表明DLNS-PCA方法相对于PCA、KPCA、FDkNN等方法对多阶段过程故障具有更高的检测率。DLNS-PCA方法提高了多阶段过程故障检测能力。

Abstract

Modern industrial products often require multiple production stages

and the fault detection of multi-stage production process has become an important issue. Multi-stage process data have the characteristics of multi center

different structure of each stage and so on. Aiming at the characteristics

a fault detection method based on double local neighborhood standardization and principal component analysis (DLNS-PCA) is proposed. Firstly

the double local neighborhood set of the sample is found. Secondly

the standard samples are obtained by using the information of the double local neighborhood set. Finally

the PCA method is used to detect the fault on the standard sample set. The DLNS can move the data centers of each stage to the same point

and adjust the degrees of dispersion of data at each stage to make its approximately equal

then multi-stage process data is fused to a single modal data following multivariate Gauss distribution. A fault detection of penicillin simulation process was carried out. The results showed that DLNS-PCA has higher fault detection rate than PCA

KPCA and FD-kNN methods. DLNS-PCA method improves the efficiency of multi-stage process fault detection.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fault detection using hierarchical variational Gaussian mixture model and principal polynomial analysis

Variable weighted principal component analysis algorithm and its application in fault detection

Principal component selection algorithm based on ReliefF and its application in process monitoring

Robust semi-supervised modelling method and its application to fault detection in chemical processes

Fault detection method for uneven-length multimode batch processes

Related Author

Yuan LI

Dongsheng YANG

Liying ZHAO

Cheng ZHANG

LAN Ting

TONG Chudong

SHI Xuhua

TAO Yang

Related Institution

College of Information Engineering, Shenyang University of Chemical Technology

Department of Science, Shenyang University of Chemical Technology

Faculty of Electrical Engineering & Computer Science, Ningbo University

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology

School of Automation and Electrical Engineering, Zhejiang University of Science and Technology

⁰