Fault isolation and identification approach in principal component subspace

WANG Haiqing; JIANG Ning

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Fault isolation and identification approach in principal component subspace

更新时间：2025-12-10

- Fault isolation and identification approach in principal component subspace
- CIESC Journal Issue 4, Pages: 659-663(2005)
- 作者机构：
  
  1. 浙江大学工业控制技术国家重点实验室，工业控制技术研究所,浙江,杭州,310027
  2. 浙江工业大学化工机械设计研究所,浙江,杭州,310032
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：25 April 2005，
  
  Published：25 April 2005
- 稿件说明：
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WANG Haiqing, JIANG Ning. Fault isolation and identification approach in principal component subspace[J]. CIESC Journal, 2005, (4): 659-663.
DOI：

WANG Haiqing, JIANG Ning. Fault isolation and identification approach in principal component subspace[J]. CIESC Journal, 2005, (4): 659-663. DOI：

摘要

主元分析 (PCA)作为数据驱动的一种统计建模方法，在化工产品质量控制与故障诊断方面得到广泛研究和应用.在故障重构技术的基础上，研究了基于T2统计量的故障分离和识别问题，分别获得了主元空间中故障可分离和识别的理论条件.以双效蒸发过程为例，对该生产过程中的10种不同故障进行仿真监测分析，证实了所获理论结果的有效性.

Abstract

Principal component analysis (PCA) has found wide application in chemical process monitoring and product quality control as a data-driven modeling method.Based on the fault reconstruction technology

the fault isolation and identification issues were explored by using T2 index

since some faults involving process fault and sensor fault that do not violate the dominant process relation described by the PCA statistical model could only be detected by T2 index.The theoretical conditions of isolatability and identifiability were obtained.The acquired results were then illustrated and verified by monitoring a simulated double-effect evaporator (DEE) process

where 10 different faults were studied.

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