Combined Multivariate Statistical Process Control

Multivariate Statistical Process Control (MSPC) methods are used commonly to detect and identify shifts in multivariate industrial processes. However, these methods are limited by assumptions and complexities. In this study, a new two-stage MSPC approach based on independent component analysis is proposed. The proposed method aims to provide solutions to both the determination and the identification of the shift in the mean vector of a multivariate process. In the first step of this new method, independent components extracted by ICA were used as monitoring statistics in the MSPC chart to detect the shift in the process. The second step of the method started to deal with the problem of identifying the source of this shift by decomposing the monitoring statistics. The simulation results show the superiority of the new method over traditional methods in both determining and identifying the shift in the process mean vector.

Process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman Process

On-line high performance liquid chromatography (HPLC) was used to monitor steady state reactions in three reactors (K = 3) over 48.0 h. Different numbers of chromatograms, with J = 1981 retention time points, were recorded for each of the three reactors. Peaks for each chromatogram were baseline corrected and aligned using correlation optimized warping (COW). To make a complete three-way data set of I = 266 chromatograms a cubic Hermite interpolation was performed. The applied bilinear multivariate statistical process control (MSPC) method included the unfolding PCA and the trilinear technique was PARAFAC. Unfolding in reactor (K) mode was the most informative. D-charts and Q-charts were applied to the data to determine samples which were out of control. Confidence limits were then applied to the D and Q-charts and variables with different behaviours from that encapsulated within the reference data set were located. Both bilinear and trilinear methods were found to be useful for process analysis.

A chemical process has a large number of measured variables, but it is usually driven by fewer essential variables, which may or may not be measured. Extracting such essential variables and monitoring them will improve the process‐monitoring performance. Independent component analysis (ICA) is an emerging technique for finding several independent variables as linear combinations of measured variables. In this work, a new statistical process control method based on ICA is proposed. For investigating the feasibility of its method, its fault‐detection performance is evaluated and compared with that of the conventional multivariate statistical process control (cMSPC) method using principal‐component analysis by applying those methods to monitoring problems of a simple four‐variable system and a continuous‐stirred‐tank‐reactor process. The simulated results show the superiority of ICA‐based SPC over cMSPC.

Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy

A multivariate approach for evaluation and monitoring of water quality in mining and minerals processing industry

Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re‐starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd.

Batch process monitoring in the original measurement's space

Monitoring a complex refining process using multivariate statistics

A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical process control (MSPC) methods. With PLS, the regression matrix is determined on the basis of a subset of the predictor variables and thus, PLS is able to reduce the number of variables to be considered. In this paper, two kinds of KPLS algorithm for construction of nonlinear regression models in possibly high-dimensional feature spaces are provided. According to the idea of structural risk minimization (SRM), the work described here provides an index of generalization errors. We give the theoretical description of the KPLS algorithms and experimentally compare the algorithms with some existing PLS and KPLS regression models. We will demonstrate that on the data sets employed KPLS achieves better results than PLS and some other KPLS. At the same time, validity of index of generalization errors is proved affirmatively.

PCA-based multivariate statistical network monitoring for anomaly detection

Chemometric quality control of chromatographic purity

This paper presents a new knowledge discovery assistance method to improve wastewater treatment plant (WWTP) operation based on multivariate statistical process control (MSPC). The proposed method combines with MSPC by principal component analysis (PCA-MSPC) and monitoring of a pre-defined performance index for efficient and stable plant operation. Fault detection and isolation (FDI) related to the performance index is selectively performed by monitoring the time series data of the performance index wherein the sample points violating the control limit of Q statistic or that of T2 statistic in PCA-MSPC are indicated. Hidden patterns of probable cause variables to deteriorate the performance index are discovered from the FDI by observing the time series data of the isolated variables. Applications of the proposed method to real WWTPs illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.