一种面向多工序复杂制造过程的质量软测量方法  

A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures

作　　者：彭开香[1,2] 秦昕 王佳浩 杨慧 PENG Kaixiang;QIN Xin;WANG Jiahao;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 Company Limited,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3-14,共12页Advanced Engineering Sciences

基　　金：国家重点研发计划项目(2021YFB3301200);国家自然科学基金项目(U21A20438,62373040,62203042)。

摘　　要：复杂制造过程关键质量变量的精准感知,是实现系统优化控制和保障系统安全稳定运行的必要前提。考虑复杂制造过程具有生产工序众多、回路互联耦合、工序质量遗传、数据时空分布等复杂特性,使得对过程质量的精准感知面临诸多困难。在此背景下,本文提出一种考虑过程时延的基于mRMR–GA–ResNet的多工序复杂制造过程质量软测量建模方法。首先,构建了一种考虑过程变量与质量变量间时延的基于最小冗余最大相关(mRMR)和遗传算法(GA)的多传感器过程变量筛选方法,以确定最优特征子集;其次,基于各工序的最优特征子集,设计了一种3维(特征–时间–工序)样本空间表征方法,工序内部以2维(特征–时间)形式表征,将工序作为通道构建3维(特征–时间–工序)样本,通过残差网络进行时间–空间特征提取,进而通过局部–全局特征融合得到最终的质量预测值;最后,通过一个实际制造过程——浮法玻璃生产过程,进行了实验验证。结果表明:在选择特征数相同的前提下,相较于其他4种基于相关性的特征选择方法(PCC、SCC、MI、MIC),本文所提多传感器过程变量筛选方法对于模型有更好的预测性能。以残差网络作为预测模型,本文所提3维样本构造方法,相较于传统的2维样本构造方法,对于模型的预测精度有了一定的提升,均方根误差ERMS、平均绝对误差EMA、对称平均绝对百分比误差E_(SMAP)分别提升9.2%、10.8%、9.8%,验证了所提方法的有效性。Objective Accurately perceiving key quality variables in complex manufacturing processes is essential for achieving system optimization control and ensuring safe and stable operation.Given the numerous production procedures,interconnected loops,quality inheritance across procedures,and the spatiotemporal distribution of data,precisely perceiving process quality presents many challenges.In this context,this study proposes a soft measurement modeling method for quality in multi-procedure complex manufacturing processes,considering process time delays,based on mRMR-GA-ResNet.Methods First,in complex manufacturing processes with continuous multi-procedure production,different process variables and quality variables exhibit varying time delays,complicating the selection of auxiliary variables.This study proposes a multi-sensor process variable selection method that considers the time delay between process and quality variables based on minimum redundancy maximum relevance(mRMR)and genetic algorithm(GA).Multi-sensor process variables are segmented by the procedure according to the process knowledge.For each procedure,the time delay effect between different procedure segments and quality variables is considered,and data alignment is performed.Using mRMR as the relevance measure,the initial feature subset for each procedure is obtained.GA is employed as the optimal feature search strategy,where the initial feature subset is binary encoded(1 for selected feature,0 for unselected feature),and an initial population is randomly generated.The fitness function is constructed by combining the prediction accuracy of the extreme gradient boosting(XGBoost)model and the number of selected features,to measure the fitness of each individual in the population.Additionally,an iterative optimization search is performed to determine the final optimal feature subset,thus deriving the auxiliary variable set for modeling.Second,considering the spatiotemporal correlation coupling and quality inheritance characteristics of complex manufacturi

关 键 词：多工序软测量 特征选择 残差网络 最小冗余最大相关 遗传算法 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]