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基于MICA-PCA的间歇过程故障监测

Keywords: 多向独立成分分析,主成分分析,间歇过程,故障监测

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Abstract:

针对具有数据非高斯分布或混合分布的间歇过程,研究一种新的改进MICA-PCA监控方法.首先利用MICA方法提取非高斯分布过程信息,通过设定负熵阈值实现独立成分个数的自动选择,以此克服传统ICA方法中需提前确定独立成分个数的缺点,再使用核密度估计方法确定相应统计量的置信限,然后对服从多元高斯分布的残差过程信息,进一步进行PCA分析和处理.将该方法应用于北京某生化制药厂重组大肠杆菌制备白介素-2发酵过程监控.结果表明:该法在过程变量不服从高斯分布的情况下能有效降低传统方法的漏报和误报率,准确地对过程进行监控.

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