| 引用本文: | 高学金,杨彦霞,王普,李晓理,常鹏,齐咏生.基于扩展核熵负载矩阵的发酵过程故障监测[J].控制理论与应用,2018,35(6):813~821.[点击复制] |
| GAO Xue-jin,YANG Yan-xia,WANG Pu,LI Xiao-li,CHANG Peng,QI Yong-sheng.Fault monitoring of fermentation process based on extended kernel entropy load matrix[J].Control Theory and Technology,2018,35(6):813~821.[点击复制] |
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| 基于扩展核熵负载矩阵的发酵过程故障监测 |
| Fault monitoring of fermentation process based on extended kernel entropy load matrix |
| 摘要点击 2366 全文点击 1021 投稿时间：2017-08-01 修订日期：2018-05-11 |
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| DOI编号 10.7641/CTA.2018.70521 |
| 2018,35(6):813-821 |
| 中文关键词 过程监测 主元分析 多阶段 发酵过程 |
| 英文关键词 process monitoring principal component analysis multistage fermentation process |
| 基金项目 国家自然科学基金项目(61640312, 61473034, 61673053, 61174109), 北京市自然科学基金项目(4172007), 北京科技新星计划交叉学科合作项目 (Z161100004916041)资助. |
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| 中文摘要 |
| 为有效降低多阶段发酵过程硬分类缺陷而导致的误报和漏报率, 本文提出了一种基于扩展核熵负载矩阵的阶
段划分策略. 首先, 将发酵过程的三维训练数据按批次方向展开成二维数据矩阵, 对每个时间片矩阵进行核熵成分分
析(kernel entropy component analysis, KECA)得到其主元和负载矩阵, 根据所得主元个数实现操作阶段的第1步划分;
之后将时间片矩阵添加到核熵负载矩阵当中得到扩展核熵负载矩阵, 计算各扩展负载矩阵间的相似度, 并用模糊C–均
值方法对其进行第二次阶段划分. 通过增加对体现生产过程改变的时间指标的考虑, 有效克服了硬化分的不足, 避免了
跳变点错分的情况. 最终将整个生产操作过程划分为不同的稳定阶段和过渡阶段, 并在划分的每一阶段中分别建
立KECA监测模型; 最后利用青霉素发酵仿真平台和大肠杆菌生产白介素–2数据进行实验. 实验结果表明该方法不但可
以准确地对生产过程进行阶段划分、降低误报率, 而且可以使生产过程故障监测的时间大大提前. |
| 英文摘要 |
| Hard classification for multistage fermentation process and cause of the defects of false alarm and alarm
failure, in order to effectively reduce the omission and the rate of false positives, this paper proposes a strategy based on
extended nuclear entropy load matrix. First, the three-mention training data array of fermentation process is unfolded in
batch ways, resulting in two-dimension forms. Then, kernel entropy component analysis (KECA) was done for each time
slice matrix to obtain its load matrix. After that, time slice matrix was added to the nuclear load matrix of entropy, and the
change of the nuclear load matrix of entropy was utilized to describe the changes of batch processes.The KECA monitoring
model was established at each stage of the division after the stage of nuclear load matrix of entropy was determined by FCM
algorithm. At last, the effectiveness and utility of the proposed method were validated through the simulation of fed-batch
penicillin and E. coli production of interleukin-2. Results showed, the proposed method could not only divide the stage and
reduce the false alarm precisely, but also detect the production difficulty more advance. |
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