基于不完整数据的氧化铝蒸发过程故障诊断方法研究

项目名称： 基于不完整数据的氧化铝蒸发过程故障诊断方法研究

项目编号： No.61273159

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 宋彦坡

作者单位： 中南大学

项目金额： 80万元

中文摘要： 由于原料成分复杂、工况波动大等因素，氧化铝蒸发过程极易发生故障。根据已积累大量数据，管道结垢或结疤会出现数据大噪音或缺失、系统参数变化的特点，研究氧化铝蒸发过程故障诊断方法。①研究不完整数据的表现形式，以及噪音评价、大噪音和缺失数据修复等数据预处理方法；②研究在子空间辨识框架下采用PCA技术直接从历史数据中设计基于奇偶性空间的残差生成器，提出基于模型框架下的故障检测与分离方法，采用多种方式设计鲁棒性策略，以适应数据不完整性特点，研究根据工况的稳定程度进行模型快速更新机制，以适应系统参数变化的特点；③采用贝叶斯网络来表达变量异常与故障类型和位置间的复杂关系，使用观测器理论研究不完整静态数学模型的快速迭代方法，利用仿真结果辅助确定贝叶斯网络结构，提出基于组合贝叶斯网络的故障决策方法；④开发氧化铝蒸发过程故障诊断系统，进行实验室和现场验证。该项目可为降低事故和能耗，提高设备寿命和利用率做出贡献。

中文关键词： 氧化铝生产；蒸发过程；故障诊断；典型相关分析；数据驱动

英文摘要： Many faults occur very easily in alumina evaporation process because of many factors such as complex components of raw material and strong fluctuant of production conditions. We will focus on the fault diagnosis methods for the complex alumina evaporation process according to the characteristics of accumulation of huge historical data, data noise or missing due to pipe scaling or scarring, and system parameters time-varying. (1) The first task is to study the behavior of incomplete data, and data preprocess methods such as evaluating data noise and recovering big noise data or missing data. (2) The second task is to study the design method of the parity space based residual generator directly from historical data using PCA technique on the basis of the framework of Subspace Identification Methods, then present the methods of fault detection and isolation on the basis of the framework of model-based FDI, employ a variety of ways to design the robust schemes in order to adapt the property of incomplete data, and study the rapid updating mechanism according to production condition stability in order to adapt the property of system structural parameters time-varying. (3) The third task is to adopt Bayesian Networks to express the complex relation between the variable abnormality and the faults types and location, st

英文关键词： alumina production；evaporation process；fault diagnosis；canonical correlation analysis；data-driven