A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes. (April 2023) - Xu, Chong; Huang, Daoping; Cai, Baoping; Chen, Hongtian; Liu, Yiqi
Abstract: Multivariate statistical process monitoring are the essential approaches to achieve better prognostics and health management (PHM) of process industries. However, incipient faults and complex behaviors (such as nonlinearity and dynamics) always render the traditional multivariate statistical process monitoring approaches inadequate. Thus, a complex-valued slow independent component analysis (CSICA) is proposed, which is able to extract optimized features from a complex-valued matrix containing both of raw data and their changing rates by resorting to a complex-valued independent component analysis operation and a batch of phase shifts. These features, named slow independent components (SICs), not only guarantee the statistical independence but also capture slowly-changing patterns, thus refining both dynamic and non-Gaussian information mostly related with incipient faults. The proposed algorithm together with novel statistics, I s 2, I f 2 and S P E, as well as their control limits can sequentially detect incipient faults effectively. Then, together with the novel differential mapping reconstructed contribution plot (DM-RCP) and Granger causality analysis, the proposed method can accurately locate rooting causes of incipient faults. Finally, the proposed framework of process monitoring is validated through two data sets from a simulation platform and an oxidation-ditch-based wastewater treatment plant, respectively. The results demonstrate that the proposed method can achieve more accurate and efficient performances than conventional methods. Highlights: A novel ICA-based framework to detect and diagnose incipient faults. Novel latent features being both statistically independent and slowly changing. Novel contribution plot being insensitive to the fault smearing effect and inherent contributions. Granger causality analysis to locate and virtualize the rooting causalities.