IFA和FA联合方法在化工过程监控中的应用
发布时间：2018-10-11 09:46
【摘要】：工业过程数据具有高斯和非高斯混合分布的特点。独立因子分析(IFA)采用一维高斯混合模型拟合任意的因子分布,因此可以处理高斯和非高斯混合的问题。虽然在给定因子数的前提下变分IFA算法可以有效地缩短建模时间,但是独立因子数的选择仍然需要较长的计算时间。此外,若IFA的因子数选择不当,会造成部分因子的信息遗留在观察变量的残差中,导致GSPE监控指标的监控性能变差。为了解决IFA在实际应用中存在的问题,本文结合了IFA和FA方法。首先使用FA辅助IFA选取独立因子数,以进一步减小IFA建模时间;其次使用FA对IFA的残差进行再处理,以解决由于独立因子数选择不当造成的问题。最后将该方法应用于田纳西-伊斯曼(TE)过程和乙烯裂解炉过程的监控中,实验结果验证了该联合方法的有效性。
[Abstract]:Industrial process data have the characteristics of mixed distribution of Gao Si and non-Gao Si. Independent factor analysis (IFA) uses a one-dimensional Gao Si mixed model to fit arbitrary factor distributions, so it can deal with the mixed problem of Gao Si and non-Gao Si. Although the variable IFA algorithm can effectively shorten the modeling time under the condition of given factor number, the selection of independent factor number still needs a long calculation time. In addition, if the factor number of IFA is not selected properly, the information of some factors will be left in the residual of observation variables, which will lead to the deterioration of monitoring performance of GSPE monitoring index. In order to solve the problem of IFA in practical application, this paper combines IFA and FA methods. In order to further reduce the modeling time of IFA, the FA aided IFA is used to select the independent factor number, and the FA is used to reprocess the IFA residuals to solve the problem caused by improper selection of independent factor numbers. Finally, the method is applied to the monitoring of Tennessee Eastman (TE) process and ethylene cracking furnace process. The experimental results show the effectiveness of the combined method.
【作者单位】： 华东理工大学化工过程先进控制和优化技术教育部重点实验室;
【基金】：国家自然科学基金(61134007,21376077,21303102) 上海市研发平台建设项目(13DZ2295300) 上海市自然科学基金(16ZR1407300)
【分类号】：TP277;TQ06
本文编号：2263746
[Abstract]:Industrial process data have the characteristics of mixed distribution of Gao Si and non-Gao Si. Independent factor analysis (IFA) uses a one-dimensional Gao Si mixed model to fit arbitrary factor distributions, so it can deal with the mixed problem of Gao Si and non-Gao Si. Although the variable IFA algorithm can effectively shorten the modeling time under the condition of given factor number, the selection of independent factor number still needs a long calculation time. In addition, if the factor number of IFA is not selected properly, the information of some factors will be left in the residual of observation variables, which will lead to the deterioration of monitoring performance of GSPE monitoring index. In order to solve the problem of IFA in practical application, this paper combines IFA and FA methods. In order to further reduce the modeling time of IFA, the FA aided IFA is used to select the independent factor number, and the FA is used to reprocess the IFA residuals to solve the problem caused by improper selection of independent factor numbers. Finally, the method is applied to the monitoring of Tennessee Eastman (TE) process and ethylene cracking furnace process. The experimental results show the effectiveness of the combined method.
【作者单位】： 华东理工大学化工过程先进控制和优化技术教育部重点实验室;
【基金】：国家自然科学基金(61134007,21376077,21303102) 上海市研发平台建设项目(13DZ2295300) 上海市自然科学基金(16ZR1407300)
【分类号】：TP277;TQ06
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