引用本文:常鹏,乔俊飞,王普,高学金.非线性和非高斯性共存的序批次反应处理过程故障诊断(英文)[J].控制理论与应用,2019,36(5):728~736.[点击复制]
CHANG Peng,QIAO Jun-fei,WANG Pu,GAO Xue-jin.Fault diagnosis of sequential batch reaction process with nonlinear and non-Gaussian coexistence[J].Control Theory and Technology,2019,36(5):728~736.[点击复制]
非线性和非高斯性共存的序批次反应处理过程故障诊断(英文)
Fault diagnosis of sequential batch reaction process with nonlinear and non-Gaussian coexistence
摘要点击 1938  全文点击 1438  投稿时间：2017-09-12  修订日期：2018-05-16
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DOI编号  10.7641/CTA.2018.70657
  2019,36(5):728-736
中文关键词  序批式反应器  多向核熵独立成分  故障检测  故障诊断
英文关键词  Sequencing Batch Reactor  Multi-way Kernel Entropy Independent Component  Fault Detection  Fault Diagnosis
基金项目  国家自然科学基金
作者单位E-mail
常鹏* 北京工业大学 changpeng@bitc.edu.cn 
乔俊飞 北京工业大学  
王普 北京工业大学  
高学金 北京工业大学  
中文摘要
      序批式反应器（SBR）的处理过程的数据具有非高斯分布和高度非线性的特点，传统特征提取方法在进行特征提取时仅仅考虑信息最大化而忽略数据的簇结构信息导致数据特征提取的不完整。由于多向核熵成分分析是一种新的监测方法，在监测过程中的应用表明能够克服传统监测方法的缺陷，减少误报警率。因此本文结合多向核熵成分分析的的优势，提出多向核熵独立成分分析方法用于SBR过程监测及故障诊断。首先，将三维SBR过程数据利用一种新的数据展开技术变为二维数据；其次，利用核熵成分分析将展开后的二维数据映射到高维空间用独立成分分析进行独立成分提取；最后提出一种基于多向核熵独立成分分析的故障诊断方法进行故障诊断。将该方法和传统方法应用于80升的SBR处理过程的监测结果表明，本文提出的方法优于传统的多向独立成分分析方法。
英文摘要
      The data of Sequencing Batch Reactor (SBR) has characteristics of non-Gaussian distribution and high nonlinearity, In order to solve the problem that SBR process monitoring algorithm can only maximize the use of data information and ignore the information in the structure of data cluster, a new Multi-way Kernel Entropy Component Analysis (MKECA) method is proposed .It also address the shortcomings of the traditional monitoring method in omission failure rate. A novel contribution analysis scheme named bar plot is developed for MKEICA to diagnose faults. The proposed MKEICA method consist of three steps: (1) the three-dimensional data of SBR is unfolded into two-dimensional by a new data expanding method. (2) Kernel Entropy Principal Component Analysis (KEPCA) is adopted to map the twodimensional data into a high dimensional feature space and use Independent Component Analysis (ICA) to extract Independent Components (ICs) in feature space.(3) in the stage of online monitoring ,bar plot is used to identify the variables causing the fault. This method is successfully applied to an 80L lab-scale SBR, and the experimental results demonstrate that, comparing with traditional MKICA, the proposed MKEICA method exhibits better performance in fault detection and diagnose .