| 特征空间递归更新的ICA算法及发酵过程监测应用 |
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| 引用本文： | 刘世成,高彦臣,王海清,李平.特征空间递归更新的ICA算法及发酵过程监测应用[J].化工学报,2008,59(11):2830-2836. |
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| 作者姓名： | 刘世成 高彦臣 王海清 李平 |
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| 作者单位： | 浙江大学工业控制技术国家重点实验室，工业控制研究所;青岛市工业信息化技术重点实验室 ; |
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| 基金项目： | 国家自然科学基金，浙江省自然科学基金 |
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| 摘 要： | 及时更新监测模型以适应过程的时变特性，对准确检测出化工过程异常和设备故障具有重要意义。针对普通独立元分析（ICA）算法在更新计算监测模型时计算复杂度高、效率低的缺点，提出了一种基于特征空间递归更新的在线独立元分析（RUFS-ICA）算法。将算法应用于青霉素发酵过程的在线建模与监测中，与普通ICA方法相比，仿真统计结果表明，平均误警率降低至1.67%，基本克服了漏报现象；与其他在线更新算法相比，复杂度明显降低，计算时间减少54.1%，节省了存储量。
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| 关 键 词： | 独立元分析 过程监测 特征空间 青霉素发酵过程 |
| 收稿时间： | 2007-12-27 |
| 修稿时间： | 2008-4-4
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ICA algorithm based on recursively updating of feature space and application to fermentation process monitoring |
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| LIU Shicheng,GAO Yanchen,WANG Haiqing,LI Ping.ICA algorithm based on recursively updating of feature space and application to fermentation process monitoring[J].Journal of Chemical Industry and Engineering(China),2008,59(11):2830-2836. |
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| Authors: | LIU Shicheng GAO Yanchen WANG Haiqing LI Ping |
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| Abstract: | Updating the monitoring model timely to address the time-variant characteristics has vital significance in detecting abnormalities of chemical process and equipment breakdown exactly.The conventional ICA-based methods used to update the model have a high computational load and low efficiency.RUFS-ICA algorithm was proposed to model and monitor a fed-batch penicillin fermentation process on line.Compared with conventional ICA methods, the proposed method reduced the false alarm rate to 1.67% and basically overcame alarm failures.Compared with other methods, the algorithm could greatly decrease the computation time by about 54.1%, and saved the memory. |
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| Keywords: | independent component analysis process monitoring feature space penicillin fermentation process |
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