Process Monitoring Using a Novel Robust PCA Scheme

Abstract

Outliers may cause model deviation and then affect the monitoring performance and hence it is a challenging problem for process monitoring. The robust principal component analysis (RPCA) approach, which describes outlier components with a sparse matrix and identifies these components using the sparse matrix recovery approach, is the most commonly used method to solve the model deviation problems caused by outliers. However, because the existing mathematical tools can only obtain a nonsparse matrix with small element values, RPCA performs poorly during process monitoring. In this paper, we propose a novel robust PCA scheme called moment-based RPCA (MRPCA). In the offline training stage, MRPCA adopts a novel outlier selection mechanism based on the difference between the higher-order and second-order central moments to select outlier samples; in the online monitoring stage, MRPCA adopts an outlier detection mechanism to distinguish outliers from fault data. Using the aforementioned mechanisms, MRPCA achieves high fault detection and low false alarm rates in tests of a numerical model and the Tennessee Eastman process.

Industrial Process Fault Detection Based on Entropy Score Contribution Analysis

“…The existence of these factors may cause variables to not conform to the law of normal distribution. Therefore, the threshold value calculated by equation (10) can not be applied to all cases. At the same time, the initial threshold obtained by determining the number of samples with a large contribution rate in algorithm 2 can also play a good role in fault detection.…”

“…At the same time, the initial threshold obtained by determining the number of samples with a large contribution rate in algorithm 2 can also play a good role in fault detection. From the analysis of equation (10), we can see that the data type of threshold m is the same as the Euclidean distance calculated from the vectors p(i, 1) =…”

“…Liu et al [9] proposed a multi-level process monitoring method for the continuous annealing process based on PCA. Lou et al [10] proposed a novel robust PCA scheme that achieve a high fault detection rate and low false alarm rate in the TE process simulation experiments. By considering the specificity of each block and the correlation between different blocks, Tong and VOLUME 4, 2016 others [11] developed an improved multi-block PCA method for the whole plant process.…”

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

“…Advancements in the field of process automation have revolutionized the chemical engineering industry to a large extent with efficient conversion of raw materials such as oil, natural gas and minerals to final products of very good quality. The emergence of smart sensor networks and distributed control systems in chemical industries for catering to continuous needs has complicated the dynamics of chemical industries [2]. Owing to these added complexities, continuous hazards such as emission discharge and explosions occur regularly in process plants.…”

Minimalist module analysis for fault detection and localization

“…As such, this paper focuses on the comparison of MMA and PCA; our conclusion is also applicable to other algorithms, such as PLS and CCA. The simulation tests in a mathematical model and the Tennessee Eastman (TE) process 26 show that MMA can successfully obtain the minimalist modules; moreover, it achieves much better performance than the traditional MSPM methods in fault detection and fault localization.…”