【摘要】 工业大肠杆菌制备过程具有非线性和非高斯性共存的特征,导致难以对故障源进行有效定位,针对这个问题,提出一种基于多向核熵独立元分析(MKEICA)的过程监测方法;同时针对传统低阶监控统计量(T2, I2和SPE)无法得到非高斯信息的不足提出了四阶累积监控统计量的方法;其次通过对四阶累积监控量进行推导,得到故障产生的原因.最后将其应用在实际的工业过程并与多向核独立元分析(MKICA)监测模型进行对比验证该方法的可行性及有效性.更多还原

【Abstract】 In the process of industrial Escherichia coli preparation, process data has both nonlinear and non-Gaussian characteristics, making it difficult to locate fault sources effectively. Aiming at this problem, a modeling method based on multiway kernel entropy independent component analysis(MKEICA) is proposed. Furthermore, in order to overcome the insufficiency of traditional low-order monitoring statistics(T~2, I~2 and SPE) to obtain non-Gaussian information, a fourthorder cumulative monitoring statistic method was proposed. In the next place, through the derivation of the fourth order cumulative monitoring statistic, the cause of the fault was obtained. For industrial validation, the feasibility and superiority of the proposed monitoring method were demonstrated in the comparison with the multiway kernel independent component anlaysis(MKICA) monitoring model.更多还原