摘要
针对间隙发酵过程具有多阶段、批次不等长,且过程动态非线性往往与发酵阶段密切相关等特点,提出一种基于多阶段动态主元分析(principal component analysis,PCA)的故障监测策略.该方法采用高斯混合模型(Gaussian mixture model,GMM)对过程数据进行聚类,能客观反映不同阶段操作模态的数据分布特点,可实现子阶段划分.针对各批次阶段划分后存在的不同步问题,采用动态时间错位(dynamic time warping,DTW)方法对各阶段进行轨迹同步,对同步后的子阶段建立动态PCA模型.最后以工业青霉素发酵过程和重组大肠杆菌制备白介素-2发酵过程为背景,采用多阶段动态PCA策略对其进行故障监测,发现算法能有效降低运行过程的漏报和误报率,验证了算法的有效性。
In industrial manufacturing,most fermentation processes are inherently multiphase and uneven-length batch processes in nature.Based on different dynamic nonlinear characteristics of different fermentation phases,a new strategy is proposed by using multi-phase dynamic principal component analysis(PCA) for fermentation process monitoring.Using Gaussian mixture model(GMM) clustering arithmetic,fermentation process data are divided into several operation stages,since GMM is adopted to discriminate different operation modes.Then,run-to-run variations among different instances of a phase are synchronized by using dynamic time warping(DTW),and sub-phase dynamic PCA models are developed for every phase.Finally,the proposed method is applied to monitor both the industrial processes of fed-batch penicillin production and interleukin-2 production in recombinant E.coli.Results demonstrate that fewer false alarms and small fault detection delay are obtained and the algorithm is proved to be efficient.
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2012年第10期1474-1481,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61174109)
内蒙古工业大学科学研究项目(ZS0201037)
关键词
发酵过程
动态时间错位
高斯混合模型
主元分析
fermentation processes
dynamic time warping(DTW)
Gaussian mixture model
principal component analysis(PCA)