一种面向复杂非平稳工业过程的质量相关分布式故障检测方法  

A Distributed Detection Method for Quality-related Faults in Complex Non-stationary Industrial Processes

作　　者：董洁[1] 李大业 尉艳梅 彭开香[1,2] 杨慧 DONG Jie;LI Daye;WEI Yanmei;PENG Kaixiang;YANG Hui(Key Laboratory of Industrial Process Knowledge Automation,Ministry of Education,School of Automation,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of the Ministry of Emergency Management of the Metallurgical Industry for Safety Risk Prevention and Control,Beijing 100083,China;Hebei Windows Glass Co.,Langfang 065000,China)

机构地区：[1]北京科技大学自动化学院工业过程知识自动化教育部重点实验室,北京100083 [2]冶金工业安全风险防控应急管理部重点实验室,北京100083 [3]河北视窗玻璃有限公司,河北廊坊065000

出　　处：《工程科学与技术》2024年第6期15-24,共10页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(62273031,U21A20483,62373040)。

摘　　要：由于复杂工业过程通常受到生产条件的变化,未知的外部干扰及其他因素的影响,其过程数据时间序列的统计特征随时间变化,呈现出非平稳特性。而工业过程的故障可能会被过程的非平稳特性所掩盖,这给复杂工业过程的质量相关故障检测带来巨大挑战。本文提出了一种基于动静特征融合且面向复杂非平稳工业过程的分布式质量相关故障检测方法。首先,利用最小冗余最大相关算法揭示和量化过程变量与质量变量之间的线性和非线性关系,并选择最具代表性的过程变量,消除所选过程变量之间的冗余性。其次,利用增广迪基–富勒检验(ADF)检验方法将所选的过程变量划分为平稳变量和非平稳变量。再次,利用工业过程的机理知识将复杂工业过程划分为多个有物理意义的子块,子块之间的信息交互通过公共变量实现,构建局部子块模型,该模型包括来自其邻居子块的信息。然后,通过偏最小二乘和长短期记忆网络方法分别提取子块中平稳变量及非平稳变量的静态特征和动态特征,并进行特征融合;利用规范变量分析算法来最大化融合后的动静态特征与质量变量之间的相关性,构建局部质量异常检测的统计量和控制限。最后,通过贝叶斯推理将局部检测结果进行融合,实现全局质量相关的故障检测。为了验证所提方法的有效性,采用河北某公司浮法玻璃生产过程的实际数据进行了实验。实验结果表明:本文所提质量相关分布式故障检测方法能够准确检测复杂非平稳过程的故障,故障检测率为100%,误报率为4%,比规范变量分析方法等具有更好的故障检测性能。综上所述,所提方法有效融合了动静特征以充分利用过程信息,在提高故障检测率同时显著降低了误报率,能够为复杂非平稳工业过程提供可靠技术支持。The statistical characteristics of process data time series in the production process typically vary over time due to changes in production conditions,unknown external interference,load fluctuations,and other factors.Hence,complex industrial processes often exhibit non-stationary characteristics.These non-stationary characteristics can obscure faults in industrial processes.Traditional quality-related fault detection methods cannot detect abnormalities on time,leading to the transfer and evolution of faults between different operational units of industrial processes.This failure threatens the quality of products and the stable operation of the production process and can also damage the economic benefits of enterprises,presenting significant challenges to the quality-related fault detection of complex industrial processes.It is essential to investigate quality-related fault detection methods for non-stationary processes,detect abnormal product quality in time,and take appropriate measures to reduce the adverse impact on enterprises’production processes and minimize their economic losses.Therefore,this study proposes a new distributed quality-related fault detection method for complex non-stationary industrial processes based on dynamic and static feature fusion.This distributed method considers the local neighborhood relationships and information interaction between sub-blocks,providing an effective process monitoring approach for complex industrial systems.Initially,the minimal redundancy maximal relevance(mRMR)algorithm is employed to reveal and quantify the linear and nonlinear relationships between process and quality variables.This classical method identifies a feature subset that maximizes the correlation between features and targets and minimizes redundancy between features based on mutual information.Process variables highly correlated with quality variables are selected using the mRMR algorithm.Simultaneously,the algorithm eliminates redundancy among the selected process variables to simplify the subseq

关 键 词：质量相关 分布式故障检测 特征提取 动静融合 复杂非平稳过程 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]