Abstract
The detection of sensor faults has proven to be easier through data-driven methods which rely on historical data collected from sensors that are placed at various locations in a process plant. Since the distribution of industrial process variables is random and non-Gaussian, the independent component analysis (ICA) method has been better suited for fault detection (FD) problems. Whenever data comes with any level of noise, there is difficulty in separating useful information, which hence degrades the monitoring quality of an FD strategy. In this paper, the robustness of FD strategies is assessed for different noise realizations of sensor data using stochastic simulations. The main objective of this work is to demonstrate that ICA-based FD strategies are more robust for different noise levels in comparison with principal component analysis (PCA). The ICA modeling algorithm is improved to avoid random initialization of a de-mixing orthogonal matrix during computation of independent components. Two case studies are considered for evaluating the robustness of FD strategies: a simulated quadruple tank process and a simulated distillation column process. Comparisons have been carried out between ICA, dynamic ICA, modified ICA and PCA strategies for different sensor noise levels. The simulation results reveal that ICA-based FD strategies over-perform PCA FD strategy in monitoring sensor faults for different levels of noise.
Highlights
An active field of research in the process control domain, process monitoring-fault detection (PM-FD) aims to quickly detect sensor faults in chemical process plants (Venkatasubramanian et al 2003; Ramakrishna Kini and Madakyaru 2020)
This includes dynamic independent component analysis (ICA) (DICA), which considers the dynamics of the data via lagged variables, modified ICA (MICA) (Tong et al 2017; Yingwei and Yang 2010), where dominant independent components (ICs) are extracted from data to reduce computational complexity, kernel ICA (KICA) (Tian et al 2009), which is used to handle both nonlinear and non-Gaussian industrial data, and multi-block ICA where ICA process monitoring is carried out in different blocks (Yingwei and Chi 2012)
Variables do not remain at a fixed state, as they have a tendency to move around a nominal operating range, and this will result in variables having strong autocorrelation
Summary
An active field of research in the process control domain, process monitoring-fault detection (PM-FD) aims to quickly detect sensor faults in chemical process plants (Venkatasubramanian et al 2003; Ramakrishna Kini and Madakyaru 2020). Many extensions of conventional ICA technique have been developed in recent years This includes dynamic ICA (DICA), which considers the dynamics of the data via lagged variables, modified ICA (MICA) (Tong et al 2017; Yingwei and Yang 2010), where dominant ICs are extracted from data to reduce computational complexity, kernel ICA (KICA) (Tian et al 2009), which is used to handle both nonlinear and non-Gaussian industrial data, and multi-block ICA where ICA process monitoring is carried out in different blocks (Yingwei and Chi 2012). The robustness of ICA and PCA fault detection strategies is validated by their ability in monitoring process data in the presence of different levels of noise. Comparisons have been carried out between ICA-, DICA-, MICA- and PCA-based fault detection strategies for different noise levels.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
