Time constrained NPE for fault detection in chemical processes

YANG Jian; SONG Bing; TAN Shuai; SHI Hongbo

doi:10.11949/j.issn.0438-1157.20160543

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Time constrained NPE for fault detection in chemical processes

更新时间：2025-11-26

- Time constrained NPE for fault detection in chemical processes
- CIESC Journal Issue 12, Pages: 5131-5139(2016)
- 作者机构：
  
  华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20160543
  CLC： P277
- Online First：05 December 2016，
  
  Published：05 December 2016
- 稿件说明：
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YANG Jian, SONG Bing, TAN Shuai, et al. Time constrained NPE for fault detection in chemical processes[J]. CIESC Journal, 2016, (12): 5131-5139.
DOI：

YANG Jian, SONG Bing, TAN Shuai, et al. Time constrained NPE for fault detection in chemical processes[J]. CIESC Journal, 2016, (12): 5131-5139. DOI： 10.11949/j.issn.0438-1157.20160543.

摘要

针对动态过程的故障检测问题，在邻域保持嵌入算法中改进邻域挑选，提出一种新的维度约简方法：时序约束邻域保持嵌入（time constrained neighborhood preserving embedding，TCNPE）算法。与邻域保持嵌入（neighborhood preserving embedding，NPE）算法只通过欧氏距离挑选邻域不同的是，TCNPE考虑到数据之间的时序相关性，在一定长度的时间窗之内采用k-近邻方法挑选邻域，并对时间窗内近邻与非近邻构造局部约束关系。首先，利用TCNPE提取数据特征，进行线性降维，然后构造T2和SPE统计量并利用密度估计（kernel density estimation，KDE）确定其控制限。最后，通过数值例子和TE过程（Tennessee-Eastman process）仿真来说明本文方法的有效性。

Abstract

For fault detection in dynamic processes

a novel dimensionality reduction method was proposed on the basis of improved neighbor selection in neighborhood preserving embedding algorithm

i.e.

time constrained neighborhood preserving embedding(TCNPE). Compared to neighborhood preserving embedding(NPE)

which selected neighborhood only by Euclidean distance

TCNPE selected neighborhoods within certain timeframe by k-nearest neighboring method with a consideration of time series correlation between data points and constructed a localized constraining relationship between near and distant neighborhoods within this time window. First

TCNPE algorithm extracted main features of the process data and performed linear dimensionality reduction. Next

Hotelling's T2 and SPE statistics were established for online process monitoring and kernel density estimation(KDE) was used to determine control limits. Case study and simulation of Tennessee-Eastman Process demonstrated efficacy of the proposed method.

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