Process Monitoring Model Research Articles

Process analysis and abnormal situation detection: from theory to practice

The article discusses the use of latent variable models based on historical data and examines their potential and limitations for improving operations for both batch and continuous processes. The use of these models for multivariate statistical process monitoring, abnormal situation detection, and fault diagnosis is demonstrated. Examples from state-of-the-art major industrial applications currently running online illustrate the tremendous potential of these methods. In this context, an industrial application for abnormal situation detection is defined as state of the art if it has been operational several years after it was commissioned, has generated large savings, has been operating safely and/or has improved safety conditions in the plant, and is accepted enthusiastically by the operators. Such an application could be based entirely on known theory, but frequently it includes company proprietary modifications to suit the particular operating characteristics of the process. The article contains an extensive literature review on the subject and practical considerations for the user, as well as warnings about potential pitfalls in areas ranging from data acquisition to modeling to online application.

Process monitoring of semiconductor thin films and interfaces by spectroellipsometry

Real-time monitoring of the growth of plasma deposited microcrystalline silicon (μc-Si:H) by multi-wavelength phase-modulated ellipsometry is presented. Several growth models for process-monitoring are reviewed. In particular the inhomogeneity in μc-Si layers is treated by allowing graded-index profile in the bulk. Using the Brüggeman effective medium theory, we describe the optical properties of μc-Si and the monitoring of the crystallinity in the upper and lower parts of the layer, and the thickness. The inversion algorithm is very fast, with calculation times within 5 s using typical PC. This method opens up ways for precise control of surface roughness, bulk thickness, and crystallization of both the top and bottom interfaces of the layer during processing devices such as solar cells and thin film transistors.

Wavelet-PLS regression models for both exploratory data analysis and process monitoring

Two novel approaches are presented which take into account the collinearity among variables and the different phenomena occurring at different scales. This is achieved by combining partial least squares (PLS) and multiresolution analysis (MRA). In this work the two novel approaches are interconnected. First, a standard exploratory PLS model is scrutinized with MRA. In this way, different events at different scales and latent variables are recognized. In this case, especially periodic seasonal fluctuations and long-term drifting introduce problems. These low-frequency variations mask and interfere with the detection of small and moderate-level transient phenomena. As a result, the confidence limits become too wide. This relatively common problem caused by autocorrelated measurements can be avoided by detrending. In practice, this is realized by using fixed-size moving windows and by detrending these windows. Based on the MRA of the standard model, the second PLS model for process monitoring is constructed based on the filtered measurements. This filtering is done by removing the low-frequency scales representing low-frequency components, such as seasonal fluctuations and other long-term variations, prior to standard PLS modeling. For these particular data the results are shown to be superior compared to a conventional PLS model based on the non-filtered measurements. Often, model updating is necessary owing to non-stationary characteristics of the process and variables. As a big advantage, this new approach seems to remove any further need for model updating, at least in this particular case. This is because the presented approach removes low-frequency fluctuations and results in a more stationary filtered data set that is more suitable for monitoring. Copyright © 2000 John Wiley & Sons, Ltd.

Mixture Principal Component Analysis Models for Process Monitoring

A mixture principal component analysis (MixPCA) model detector is proposed. The detector creates each cluster by aggregating input exemplars. The number of clusters in the data set is automatically determined by a Gaussian filter technique called heuristic smoothing clustering. The structure building contains two stages: (1) sizing each cluster on the basis of the clustering analysis and (2) setting up the multivariable statistic confidence levels for each cluster. After the clusters are identified, MixPCA, which is a group of PCA models, will then be created. If any input pattern is under normal operation, it would fall into the accepted region whose criteria are set on the basis of the principal component analysis (PCA), T2 and Q charts. In addition, the techniques of similarity measuring and merging between clusters are also developed for the new correlation structure of the process variables when new data sets are included. The proposed model development procedure is favorable because of its simplici...

PCA of wavelet transformed process data for monitoring

Producing a uniform product is important for several reasons such as maintenance of a competitive position, reduction in the number of shutdowns and startups, and the elimination of the sources of variability. Multivariate statistical methods can assist in the identification of process correlations and the development of process monitoring models. This work extends these concepts by demonstrating that the correlations and resulting monitoring models can be improved greatly with the addition of pre-filtering the time signals using a median filter, and time-scale decomposition using a multi-resolution wavelet function. After the data are filtered and decomposed, the multivariate statistical method of principal component analysis (PCA) is used to develop a process monitoring model. Data that was taken from a difficult-to-operate industrial process are used to demonstrate these ideas.