PICA based process monitoring method

GE Zhiqiang; SONG Zhihuan

您当前的位置：

首页 >

文章列表页 >

PICA based process monitoring method

更新时间：2025-12-10

- PICA based process monitoring method
- CIESC Journal Issue 7, (2008)
- 作者机构：
  
  浙江大学工业控制技术国家重点实验室,工业控制研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 July 2008，
  
  Published：05 July 2008
- 稿件说明：
移动端阅览
GE Zhiqiang, SONG Zhihuan. PICA based process monitoring method[J]. CIESC Journal, 2008, (7).
DOI：

GE Zhiqiang, SONG Zhihuan. PICA based process monitoring method[J]. CIESC Journal, 2008, (7). DOI：

摘要

工业过程中普遍存在噪声污染，本文在概率主元分析方法（PPCA）的基础上，把该方法推广到非高斯过程，提出一种新的基于概率独立成分分析（PICA）的过程监控方法.针对过程的非高斯和噪声信息，分别建立其对应的统计量I2和MR.通过对Tennessee Eastman（TE）过程的仿真研究，验证了该方法的可行性和有效性，较好地改善了过程的监控效果，从而更好地保证过程运行的安全、稳定性.

Abstract

Noise corruption always exists in the industrial process. Based on the probabilistic principal component analysis (PPCA) method

a new process monitoring method based on probabilistic independent component analysis (PICA)was proposed

which extends PPCA to the non-Gaussian process. Two statistical quantities (I2 and MR) were constructed for monitoring non-Gaussian and noise information of the process. A case study of the Tennessee Eastman (TE) process showed that the proposed method was feasible and efficient. The process monitoring performance was evidently improved

thus enhancing the reliability and stability of the TE process.

关键词

Keywords

references

Views

248

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Recent advances and perspectives on sulfide-based photocatalysts for hydrogen production

Exothermic characteristics of energy carrier for calcium looping thermo-chemical energy storage

Prediction of biomass pyrolysis product yield based on machine learning algorithms

Hydrolysis reaction mechanism of CNCl on the surface of composite metal CuCr2O4

Thermodynamic performance of tower-type solar-assisted supercritical compression CO2 energy storage system

Related Author

No data

Related Institution

No data

⁰