工业过程动态数据的多模型在线重构研究

项目名称： 工业过程动态数据的多模型在线重构研究

项目编号： No.61503075

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 陈磊

作者单位： 东华大学

项目金额： 19万元

中文摘要： 工业4.0背景下以大规模动态数据为主的复杂工业过程, 迫切需要系统地分析具体工况，提出其在线动态建模方法。本项目紧紧围绕工业过程动态数据建模的发展方向，分别针对动态数据含有未知扰动、遗失数据以及时间延迟等难题，系统地开展基于多模型方法的在线重构方法研究。提出基于多模型方法的结构简单、参数时变、易于应用的全局过程模型来描述工业过程；基于离线数据建立的过程模型，分别针对局部模型和权重函数实现过程模型依据动态数据在线自适应重构，使模型更加符合系统的动态特性；提出过程动态数据含有未知扰动的在线交替重构、含有遗失数据的在线递推重构和含有时间延迟的在线转移重构等方法。选取纺织纤维加工系统的典型工业过程，对提出的多模型在线重构方法进行验证。本项目成果对于复杂工业过程的大规模动态数据建模具有重要价值，并对其提高生产效率、提升产品质量、实现节能减排等具有重要的理论和实际意义。

中文关键词： 在线建模；数据驱动建模；系统辨识；模型近似；混合建模

英文摘要： Under the background of industry 4.0, complex industrial processes with large-scale dynamic data are in urgent need of dynamic modeling method based on systematic analyzing the operating conditions. With regard to the industrial process modeling with dynamic data, and the data exhibit uncertain disturbance, missing data and time delay, this program is designed for carrying out the online reconstruction based on the multiple model approach. The aim is to put forward a new global process model with simple model structure and time-varying parameters. The main contents of this program will cover the scopes as below. Firstly, the process model based on the offline data is developed, and to optimize and adaptive reconstruct the process model, the local models as well as the weighting functions are reconstructed, respectively. Secondly, online alternate reconstruction approach is proposed for the dynamic data with uncertain disturbance; recursive reconstruction approach for the dynamic data with missing data; transfer reconstruction approach for the dynamic data with time delay. Meanwhile, the textile fiber processing system as a typical industrial process is applied to further validate the effectiveness of the proposed approach. With the achievements above, the program has important theoretical and realistic significance, and would enhance productivity, improve the quality of the product and achieve energy conservation and emissions reduction.

英文关键词： online modeling;data-driven modeling;system identification;approximate model;mixed modeling