摘要
流程工业过程中存在难以在线检测的关键生产运行指标,且工业数据表现出多模态特征,采用传统的基于回归的预报模型需要增加模型复杂度,易导致过拟合问题。因此,提出一种基于多层截断高斯混合回归模型的终点预报方法。首先,筛选流程工业过程中与关键生产运行指标相关的关键变量;然后,引入多层树形结构来表达数据的多模态结构,将关键变量与关键指标的历史数据利用一种自顶向下的训练算法,自适应地根据数据分布训练出多层截断高斯混合回归模型;最后,利用训练模型预报关键指标。以两组工业数据为实验对象进行了实验,结果表明,该终点预报方法在过程变量预报方面具有良好的准确性。
In the process industry,the presence of critical production operation indicators that are challenging to monitor online,coupled with the multimodal nature of industrial data,necessitates heightened complexity in traditional regression-based predictive models,thereby increasing the risk of overfitting.A method for terminal prediction via a hierarchical truncated Gaussian mixture regression model is proposed.Initially,essential variables pertaining to critical production operation indicators in the process industry are identified,followed by the introduction of a hierarchical tree structure to represent the multimodal data structure.Historical data of essential variables and indicators are utilized to build a hierarchical truncated Gaussian mixture regression model using a top-down training procedure,adapting to data distribution.Finally,the training model is utilized to forecast important indicators.Two sets of industrial data were utilized as experimental objects,and the findings demonstrated that the terminal prediction approach had excellent accuracy in predicting process variables.
作者
蒋鹏
卢绍文
JIANG Peng;LU Shaowen(Kunming Shipborne Equipment Research&Test Center,Kunming 650051,China;State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819,China)
出处
《控制工程》
北大核心
2025年第12期2253-2262,共10页
Control Engineering of China
基金
国家自然科学基金辽宁联合基金重点项目(U24A20275)。
关键词
终点预报
多模态特征
多层截断高斯混合回归
树形结构
Terminal prediction
multimodal nature
hierarchical truncated Gaussian mixture regression
tree structure
