知识与潜结构映射技术相结合的多单元串联过程故障诊断方法及应用

项目名称： 知识与潜结构映射技术相结合的多单元串联过程故障诊断方法及应用

项目编号： No.61304107

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 刘强

作者单位： 东北大学

项目金额： 23万元

中文摘要： 本项目以难以建立机理模型的多单元串联连续过程为背景，进行知识与潜结构映射相结合的故障诊断方法研究与应用研究。由于传统潜结构映射故障诊断方法未利用过程运行知识，存在对过程故障的解释能力不强、无法准确诊断故障原因等不足。本项目结合多单元连续过程的纵向分层、横向分级的过程运行知识，建立知识与潜结构映射技术相结合的分层次逐级诊断方法及实现技术，具体包括：（1）研究过程层内纵向分层知识与潜结构映射相结合的分层诊断方法；（2）研究单元划分、变量采样位置的横向分级知识与潜结构映射相结合的逐级诊断方法；（3）研究物料传输知识与潜结构映射相结合的物料传输故障诊断方法；（4）上述方法在钢铁生产连续退火过程进行应用研究。开展本研究将提高传统方法对故障原因的诊断能力和对故障的解释能力，为在此基础上的视情维护打下基础。从而，提高我国复杂过程安全运行水平，避免灾难事故发生、保证产品质量，取得社会和经济效益。

中文关键词： 多单元串联过程；过程运行知识；多元统计过程监控；故障诊断；潜结构映射

英文摘要： This project aims at establishing hybrid data-driven fault diagnosis methods with projection to latent structures incorporating process operation knowledge for multi-unit sequential manufacturing processes, which are difficult to be modeled mathematically. When achieving fault diagnosis for such processes, traditional projection to latent structures based fault diagnosis methods encounter two problems, i.e. difficulty in diagnosing cause of fault, and lack of interpretation for the process faults. In this project, the process operation knowledge on vertical hierarchical-organized and horizontal block-wise structures of the sequential manufacturing processes are utilized to establish hierarchical and decentralized mixted fault diagnosis theory and realization techniques. The main work includes 1) developing hierarchical fault diagnosis method while incorporating vertical hierarchical-organized knowledge into the latent structures decomposition within process layer; 2) investigating decentralized fault diagnosis method while incorporating horizontal block-wise knowledge on unit partition and sampling location into the latent structures decomposition; 3) obtaining material-transportation fault identification method when incorporating material-transportation knowledge into the latent structures decomposition; 4) ap

英文关键词： Multi-unit sequential manufacturing processes；Process operation knowledge；Multivariate statistical process monitoring；Fault diagnosis；Projection to latent structures