Fault detection method for uneven-length multimode batch processes

GUO Jinyu; YUAN Tangming; LI Yuan

doi:10.11949/j.issn.0438-1157.20151157

您当前的位置：

首页 >

文章列表页 >

Fault detection method for uneven-length multimode batch processes

更新时间：2025-11-26

- Fault detection method for uneven-length multimode batch processes
- CIESC Journal Issue 7, Pages: 2916-2924(2016)
- 作者机构：
  
  沈阳化工大学信息工程学院,辽宁,沈阳,110142
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151157
  CLC： P277
- Online First：05 July 2016，
  
  Published：05 July 2016
- 稿件说明：
移动端阅览
GUO Jinyu, YUAN Tangming, LI Yuan. Fault detection method for uneven-length multimode batch processes[J]. CIESC Journal, 2016, (7): 2916-2924.
DOI：

GUO Jinyu, YUAN Tangming, LI Yuan. Fault detection method for uneven-length multimode batch processes[J]. CIESC Journal, 2016, (7): 2916-2924. DOI： 10.11949/j.issn.0438-1157.20151157.

摘要

提出一种不等长的多模态间歇过程故障检测方法。首先，运用局部加权算法对不等长批次数据进行预处理。在训练样本中确定不等长数据的最大可保留长度，利用k近邻信息，通过加权重构出不等长批次缺失的数据点。其次，对等长的训练集构造局部近邻标准化矩阵，运用K-means算法进行模态聚类，使用局部离群因子方法确定第一控制限，并剔除离群样本。最后，对各个模态建立MPCA模型并确定第二控制限。根据各个模态控制限的匹配系数计算统一的统计量和控制限，在统一的控制限下进行多模态故障检测。将提出方法应用于半导体工业过程，仿真结果表明，与传统的故障检测算法相比，本文算法提高了故障检测率，验证了该方法的有效性。

Abstract

A fault detection algorithm for uneven-length multimode batch processes is proposed. First

the local weighted algorithm is used to preprocess the uneven-length batch data. In the training sample

the maximum retention length of uneven-length data is determined. Using the k-nearest neighbor information

the missing data points are reconstructed by weighting reconstruction. Secondly

the local neighbor normalized matrix is estimated for the training set of equal length. The K-means algorithm is used to classify the models. The local outlier factor method is used to determine the first control limits and remove outliers. Finally

the MPCA model is established and the second control limits are determined for each model. The unified statistics and control limits are calculated according to the matching coefficients of the control limit of the various models. The multimode fault detection is carried out under the unified control limits. The algorithm is applied to the semiconductor industrial process. Simulation results show that the proposed algorithm improves the fault detection rate relative to the traditional fault detection algorithms. The effectiveness of the proposed method is verified.

关键词

Keywords

references

Views

311

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fault diagnosis based on PCA method with multi-block information extraction

Fault detection method based on state space-PCANet

DLNS-PCA-based fault detection for multimode batch process

Fault detection method based on minimum sufficient statistics pattern analysis

A process monitoring method based on informative principal component subspace reconstruction

Related Author

Bingbin GU

Weili XIONG

DONG Shun

LI Yiguo

SUN Shuanzhu

LIU Xichui

SHEN Jiong

FENG Liwei

Related Institution

School of Internet of Things Engineering, Jiangnan University

Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University

School of Energy and Environment, Southeast University

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University

Jiangsu Frontier Electric Technology Company Limited

⁰