非平稳过程异常监测方法:综述与展望
Overview and prospect of abnormal monitoring methods for non-stationary processes
王敏 1冯智彬 1吴德浩 2张景欣 3周东华4
作者信息
- 1. 电子科技大学自动化工程学院,成都 611731
- 2. 中南大学自动化学院,长沙 410083
- 3. 东南大学自动化学院,南京 210096
- 4. 山东科技大学电气与自动化工程学院,青岛 266000;清华大学自动化系,北京 100018
- 折叠
摘要
实际工业过程受多种因素(如原材料变化、负载波动、设备老化等)的影响,往往表现出非平稳特性,即过程监测数据统计特性随时间发生变化,因此非平稳过程异常监测备受关注并已成为监测领域的焦点之一.本文从监测方法的角度对非平稳过程异常监测相关研究成果进行了系统性的回顾:首先对非平稳过程的概念和技术难点进行了概述;其次,将非平稳过程监测方法根据原理的差异划分为五大类,并总结了各类方法的优点与不足;最后,结合当前技术发展的现状,对非平稳过程研究中的挑战进行了深入分析与展望.
Abstract
The actual industrial processes are often affected by various factors,such as changes in raw materials,fluctuations in workload,and aging equipment.These processes show non-stationary characteristics,meaning that the statistical properties of the data used to monitor them change over time.This has led to a significant focus on monitoring non-stationary processes in the field of process monitoring.This paper presents a systematic review of research achievements in non-stationary process anomaly monitoring,focusing on the different monitoring methods.It first explains the concept of non-stationary processes and the technical challenges associated with them.Then,it categorizes non-stationary process monitoring methods into five major types based on their principles.The paper then summarizes the advantages and limitations of each type of method.Finally,it conducts an in-depth analysis of the current state of technological development and provides an outlook on the challenges in non-stationary process monitoring.
关键词
非平稳过程/过程监测/自适应建模/协整分析/平稳子空间分析/慢特征分析/深度学习Key words
non-stationary processes/process monitoring/adaptive modeling/cointegration analysis/stationary subspace analysis/slow feature analysis/deep learning引用本文复制引用
基金项目
国家自然科学基金项目(62303090)
国家自然科学基金项目(62033008)
出版年
2024
NETL
NSTL
万方数据