连续与离散变量协同分析的非平稳非高斯工业过程异常检测

蔡美玲; 罗迪; 肖敬日; 李婧妍; 刘金平

doi:10.12263/DZXB.20240466

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连续与离散变量协同分析的非平稳非高斯工业过程异常检测

学术论文 | 更新时间：2026-05-07

- 连续与离散变量协同分析的非平稳非高斯工业过程异常检测
- Continuous and Discrete Variables-Concurrent Analysis-Based Nonstationary and Non-Gaussian Industrial Process Anomaly Detection
- 电子学报 2024年52卷第10期页码：3291-3300
- 作者机构：
  
  1.湖南师范大学信息科学与工程学院，湖南长沙 410081
  2.湘江实验室，湖南长沙 410205
  3.湖南师范大学数学与统计学院，湖南长沙 410081
- 作者简介：
  
  [ "蔡美玲女，1982年3月出生于湖南省岳阳市.现为湖南师范大学信息科学与工程学院副教授.主持、参与国家和省部级科研课题10余项.在国内外发表学术论文20余篇.E-mail: Cai.Meiling@hunnu.edu.cn" ]
  [ "罗迪女，2001年9月出生于河南省南阳市.现为湖南师范大学软件工程专业硕士研究生，主要研究方向为模式识别、工业故障检测与诊断.中国电子学会会员编号：E190158141A.E-mail: wsld@hunnu.edu.cn" ]
  [ "肖敬日男，2002年4月出生于湖南省邵阳市.现为湖南师范大学软件工程专业硕士研究生.主要研究方向为智能故障诊断.E-mail: 201930222056@hunnu.edu.cn" ]
  [ "李婧妍女，1998年2月出生于河南省安阳市.目前在湖南师范大学数学与统计学院攻读博士学位.主要研究方向为工业过程智能监测.E-mail: jingyanlili@163.com" ]
  [ "刘金平男，1983年9月出生于湖南省邵阳市.现为湖南师范大学信息科学与工程学院教授、博士生导师.主持、参与国家和省部级科研课题10余项.发表国际高水平SCI期刊论文60余篇，中文权威期刊论文20余篇，申请国家发明专利40余项，已授权20项.E-mail: lip@phunnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62371187);湘江实验室重大项目(22XJ01013)
- DOI：10.12263/DZXB.20240466
  中图分类号： TP273
- 收稿：2024-05-20，
  
  修回：2024-08-30，
  
  纸质出版：2024-10-25
- 稿件说明：
移动端阅览
蔡美玲, 罗迪, 肖敬日, 等. 连续与离散变量协同分析的非平稳非高斯工业过程异常检测[J]. 电子学报, 2024, 52(10): 3291-3300.

CAI Mei-ling, LUO Di, XIAO Jing-ri, et al. Continuous and Discrete Variables-Concurrent Analysis-Based Nonstationary and Non-Gaussian Industrial Process Anomaly Detection[J]. Acta Electronica Sinica, 2024, 52(10): 3291-3300.
蔡美玲, 罗迪, 肖敬日, 等. 连续与离散变量协同分析的非平稳非高斯工业过程异常检测[J]. 电子学报, 2024, 52(10): 3291-3300. DOI：10.12263/DZXB.20240466

CAI Mei-ling, LUO Di, XIAO Jing-ri, et al. Continuous and Discrete Variables-Concurrent Analysis-Based Nonstationary and Non-Gaussian Industrial Process Anomaly Detection[J]. Acta Electronica Sinica, 2024, 52(10): 3291-3300. DOI：10.12263/DZXB.20240466

摘要

工业过程数据涵盖离散和连续变量，它们之间内在的统计分布特性是揭示系统运行状况的关键.然而，现有的监测模型多聚焦于高斯假设下的连续过程变量，忽略了离散变量、连续变量的多模分布特性以及数据中的噪声、离群点对模型的影响，难以精准反映数据的真实分布特性，对非高斯、非平稳过程的异常检测效果欠佳.因此，本文提出一种鲁棒的工业过程异常检测方法——连续和离散变量协同分析的变分贝叶斯混合判别方法（Continuous and Discrete Variables-Concurrent Analysis-based Variational Bayesian Mixture Discriminant，CDVCA-VBMD）.通过构建基于变分贝叶斯推断的面向连续变量的混合学生分布模型与面向离散多变量的混合多项分布模型，有效解决过程变量非高斯分布特性难以有效提取的问题，实现了过程变量复杂相关性的高效处理与分析；同时，在过程监测中引入了持续学习的理念，确保对非平稳时变工业过程异常的有效检测.以数值仿真系统和田纳西·伊斯曼过程为例，进行了大量验证性和对比性实验.结果表明，CDVCA-VBMD能准确估计非平稳工业过程多变量的混合多模分布特性，并对数据中的噪声和离群点具有较强的鲁棒性，从而实现工业异常的准确检测，为非高斯复杂工业过程的长期、鲁棒监测提供了有力支持.

Abstract

Industrial process data encompasses continuous and discrete variables

whose underlying statistical characteristics are crucial for revealing operational conditions. However

current process monitoring models predominantly focus on continuous variables with Gaussian assumptions

which often overlook the significant effects of the multimodal distribution characteristics of process variables

as well as the noises and outliers in process data. These limitations hinder the models' ability to capture complex statistical characteristics

leading to low detection performance particularly in non-Gaussian and nonstationary processes. This article introduces a robust anomaly detection method termed continuous and discrete variables-concurrent analysis-based variational Bayesian mixture discriminator (CDVCA-VBMD). It models continuous variables with a mixed student's t-distribution and discrete variables with a mixed multinomial distribution based on variational Bayesian inference

which can adeptly manage and analyze the complex interdependencies between process variables and overcome the non-Gaussian nature of continuous variables effectively. Furthermore

CDVCA-VBMD incorporates continuous learning to ensure the effective detection of nonstationary industrial processes. Extensive validation and comparative experiments were conducted on a numerical simulation system and the Tennessee Eastman (TE) process. The outcomes demonstrate that CDVCA-VBMD can accurately characterize the mixed multimodal distribution characteristics of time-varying industrial processes

facilitating accurate anomaly detection. Additionally

the method exhibits robustness against noise and outliers in process data

supporting long-term and reliable monitoring of complex and non-Gaussian industrial processes.

关键词

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