Batch process monitoring using on-line MIR spectroscopy

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.

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

Statistical process control (SPC) charts are powerful tools that are used to improve quality, increase uniformity and minimize production costs in manufacturing. Neural networks (NNs) have been successfully applied to interpret univariate SPC charts. Despite the growing importance and popularity of multivariate SPC (MSPC), the published literature on the application of NNs to SPC has not adequately discussed the issues related to application of NNs to MSPC for pattern recognition. This paper uses the chi-squared ( x2) statistic as a compact format for representing the input to NNs specialized in positive identification of selected subclasses of multivariate non-random patterns. It is imperative to maintain the reliability of decisions by such specialist NNs in order to convince process operators of their benefits. Therefore, to address the above requirement, this report also shows how novelty detection (ND) can be used as a filter to allow only relevant abnormal patterns to be processed by appropriate specialist NNs. Finally, a new framework within which specialist NNs can complement the decisions of ND and other MSPC techniques is outlined.

Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates

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

Chemometric quality control of chromatographic purity

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables; a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

Batch process monitoring in the original measurement's space

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

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections.

This article develops a new multivariate statistical process control (SPC) methodology based on adapting the LASSO variable selection method to the SPC problem. The LASSO method has the sparsity property of being able to select exactly the set of nonzero regression coefficients in multivariate regression modeling, which is especially useful in cases where the number of nonzero coefficients is small. In multivariate SPC applications, process mean vectors often shift in a small number of components. Our primary goals are to detect such a shift as soon as it occurs and to identify the shifted mean components. Using this connection between the two problems, we propose a LASSO-based multivariate test statistic, and then integrate this statistic into the multivariate EWMA charting scheme for Phase II multivariate process monitoring. We show that this approach balances protection against various shift levels and shift directions, and thus provides an effective tool for multivariate SPC applications. This article has supplementary material online.

Experiences with industrial applications of projection methods for multivariate statistical process control

Combined Multivariate Statistical Process Control

Multivariate Statistical Process Control (MSPC) techniques are effective tools for detecting the abnormalities of multivariate process variation. MSPC techniques are based on overall statistics; this has caused the difficulties in interpretation of the alarm signal, that is, MSPC charts do not provide the necessary information about which process variables (or subset of them) are responsible for the signal, and this task is left up to the quality engineers in production field. This article proposes a model based on LS-SVM pattern recognizer to diagnose the bivariate process abnormality in covariance matrix. The main property of this model is a supplement of MSPC |S| chart to identify the variable(s) which is (are) responsible for the process abnormality when |S| chart issue a warning signal. Through simulation experiment, the performance of the model is evaluated by accuracy rate of pattern recognition. The results indicate that the proposed model is an effective method to interpret the root causes of the process abnormality. A bivariate example is presented to illustrate the application of the proposed model.

Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes