Statistical Fault Detection of Chemical Process - Comparative Studies | Abstract
Journal of Chemical Engineering & Process Technology

Journal of Chemical Engineering & Process Technology
Open Access

ISSN: 2157-7048


Statistical Fault Detection of Chemical Process - Comparative Studies

Majdi Mansouri, Mohammed ZS, Raoudha Baklouti, Mohamed Nounou, Hazem Nounou, Ahmed Ben Hamida and Nazmul Karim

This paper addresses the statistical chemical process monitoring using improved principal component analysis (PCA). PCA-based fault-detection technique has been used successfully for monitoring systems with highly correlated variables. However, standard PCA-based detection charts, such as the Hotelling statistic, T2 and the sum of squared residuals, SPE, or Q statistic, are not able to detect small or moderate events since they use only data from the most recent measurements. Different fault detection (FD) charts, namely generalized likelihood ratio test (GLRT), shewhart control chart and exponentially weighted moving average chart (EWMA) control chart have been shown to be among the most effective univariate fault detection methods and more suitable for detection small faults. The objective of this work is to improve the PCA-based fault detection by using more sophisticated FD charts to achieve further improvements and widen the applicability of the process monitoring techniques in practice. The PCA presented here is investigated as modeling algorithm in the phase of fault detection. The fault detection problem is addressed so that the data are first modeled using the PCA algorithm and then the faults are detected using FD chart. The detection stage is related to the evaluation of detection charts, which are declares the presence of the fault. Those charts are computed using the PCA-based residual. The fault detection performance is illustrated through a simulated continuously stirred tank reactor (CSTR) data. The results demonstrate the effectiveness of the PCA-based FD chart methods for detecting the single and the multiple sensor faults.