The process monitoring of industries by means of multivariate statistical methods has gained popularity in academic and industrial communities. However, nonlinearity, autocorrelation, and high dimensionality can render traditional approaches inadequate. This necessitates a more powerful method for implementing better process monitoring in reality. Therefore, a novel independent component analysis (ICA) algorithm, termed complex dynamic independent component analysis (CD-ICA), is proposed for information refinement and feature extraction in this paper. This proposed algorithm extracts dominant features from a complex-valued matrix containing raw data information and their changing rates through complex ICA and properly shifting phase operations. The novel fault detection index, based on the three traditional monitoring statistics of the ICA algorithm, is enhanced and applied to monitor real chemical and biological processes efficiently by combining the aforementioned algorithm. The performance of the presented method is assessed based on data from a simple multivariate mathematical simulation and data from a real wastewater treatment plant (WWTP). The results show that this approach provides higher efficiency and performance than traditional approaches.

中文翻译：

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由复杂的独立成分分析算法增强的新型过程监控方法，适用于废水处理

借助多元统计方法对工业进行过程监控已在学术界和工业界流行起来。然而，非线性、自相关和高维度会使传统方法变得不合适。这需要一种更强大的方法来在现实中实现更好的过程监控。因此，本文提出了一种新的独立分量分析（ICA）算法，称为复杂动态独立分量分析（CD-ICA），用于信息细化和特征提取。该算法通过复杂的 ICA 和适当的移相操作从包含原始数据信息及其变化率的复值矩阵中提取主要特征。新颖的故障检测指标，基于 ICA 算法的三个传统监测统计量，通过结合上述算法，增强并应用于有效监测真实的化学和生物过程。所提出方法的性能是根据来自简单多元数学模拟的数据和来自真实污水处理厂 (WWTP) 的数据进行评估的。结果表明，这种方法比传统方法提供了更高的效率和性能。