基于时空近邻标准化和PCA的故障检测方法

doi:10.3969/j.issn.2095-2198.2024.01.008

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摘要

针对多阶段复杂工况数据非线性、动态性的特点，提出了基于时空近邻标准化和主元分析相结合的故障检测方法（TSNS-PCA）.首先在时间层次上搜索样本的近邻集，其次寻找样本的时间近邻在空间层次上的近邻样本集，然后在标准化样本集上通过主元分析方法计算监控模型的T²和SPE控制限，最后计算待检测样本的相应统计值，并且与控制限相比较，达到故障检测目的.TSNS方法不仅可以将复杂非线性、动态性、多阶段数据中心变换至坐标原点，并使新数据近似服从单一正态分布，还保持了离群点与正常点间的偏离程度.利用一个非线性动态数值模拟过程和青霉素仿真过程证明在动态多阶段过程监控中TSNS-PCA方法的效果明显优于PCA、KPCA、DPCA、WkNN、TSNS-LOF等方法.

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关键词: 故障检测')" href="#">

故障检测标准化主元分析 k近邻动态性

Abstract:

With the development of industrial technology,industrial processes usually have multiple production stages.Therefore,the study of multi-stages fault detection methods is of great significance.Multi-stage complex working condition data has non-linear and dynamic characteristics.Aiming at the characteristics of the above data,a fault detection method based on the combination of time-space local nearest neighborhood standardization and principal component analysis(TSNS-PCA)is proposed.Firstly,the nearest neighbor set of the sample is searched at the time level,secondly,the nearest neighbor sample set of the time nearest neighbor of the sample is found at the space level,and then the T² and SPE control limits of the monitoring model are calculated on the standardized sample set by the principal component analysis method.Finally,the corresponding statistical value of the sample to be detected are calculated and compared with the control limits to achieve the purpose of fault detection.The TSNS method can transform the complex nonlinear,dynamic and multi-stage data to the origin of coordinates and make the new data approximately to a single normal distribution,while maintaining the degree of deviation between outliers and normal points.A non-linear dynamic numerical example and penicillin simulation process are uesd to prove that the effect of TSNS-PCA method is significantly better than that of PCA,LNS-PCA,DPCA,WkNN,TSNS-LOF and other methods in dynamic multi-stage process monitoring.

Key words: fault detection')" href="#">

fault detection standardization principal component analysis k nearest neighbor rule dynamic

出版日期: 2024-02-29 发布日期: 2024-09-23 整期出版日期: 2024-02-29

ZTFLH:

TP277

基金资助:

国家自然科学基金重大项目资助项目（61490701）；国家自然科学基金资助项目（61673279）

作者简介: 李元（1964—），女，辽宁沈阳人，教授，博士，主要从事基于数据驱动复杂过程故障诊断的研究.

引用本文:

李元1, 刘雨田1, 冯立伟1, 2.

基于时空近邻标准化和PCA的故障检测方法 [J]. 沈阳化工大学学报, 2024, 38(1): 52-60.
LI Yuan1, LIU Yutian1, FENG Liwei1, 2.

Fault Detection Method Based on Time-Space Neighborhood Standardization and Principal Component Analysis . Journal of Shenyang University of Chemical Technology, 2024, 38(1): 52-60.

链接本文:

https://xuebao.syuct.edu.cn/CN/10.3969/j.issn.2095-2198.2024.01.008 或 https://xuebao.syuct.edu.cn/CN/Y2024/V38/I1/52

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[1]	张成1, 赵丽颖2, 杨东昇2, 李元2. 基于LPP特征空间重构的故障检测策略 Fault Detection Strategy Based on the Feature Space Reconstruction of LPP [J]. 沈阳化工大学学报, 2023, 37(5): 472-471.
[2]	郭金玉, 刘玉超, 李元. 加权局部近邻标准化PCA的工业过程故障检测 [J]. 沈阳化工大学学报, 2021, 35(3): 265-274.

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