he 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.