Multivariate SPC Charts for Monitoring Batch Processes

Nomikos, Paul; MacGregor, John F.

doi:10.1080/00401706.1995.10485888

Cited by 1,155 publications

(830 citation statements)

References 40 publications

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“…In industrial applications, this can often be reasonably well achieved by taking observations sufficiently far apart in time. For syndromic surveillance data that exhibit characteristics such as seasonal cycles or other trends, which we were frankly surprised not to find in our data, other methods such as the EWMA or those proposed by Nomikos and MacGregor [NM95] might be more appropriate and effective.…”

Section: Applying Spc To Syndromic Surveillancementioning

confidence: 97%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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Since the terrorist attacks on September 11, 2001, many state and local health departments around the United States have started to develop syndromic surveillance systems. Syndromic surveillance -a new concept in epidemiology -is the statistical analyses of data on individuals seeking care in emergency rooms (ER) or other health care settings with preidentified sets of symptoms thought to be related to the precursors of diseases. Making use of existing health care or other data, often already in electronic form, these systems are intended to give early warnings of bioterrorist attacks or other emerging health conditions. By focusing on symptoms rather than confirmed diagnoses, syndromic surveillance aims to detect bioevents earlier than would be possible with traditional surveillance systems. Because potential bioterrorist agents such as anthrax, plague, brucellosis, tularemia, Q-fever, glanders, smallpox, and viral hemorrhagic fevers initially exhibit symptoms ("present" 142 Michael A. Stoto et al. in medical terminology) of a flulike illness, data suggesting a sudden increase of individuals with fever, headache, muscle pain, and malaise might be the first indication of a bioterrorist attack or natural disease outbreak. Syndromic surveillance is also thought to be useful for early detection of natural disease outbreaks [Hen04].Research groups based at universities, health departments, private firms, and other organizations have proposed and are developing and promoting a variety of surveillance systems purported to meet public health needs. These include methods for analysis of data from healthcare facilities, as well as reports to health departments of unusual cases. Many of these methods involve intensive, automated statistical analysis of large amounts of data and intensive use of informatics techniques to gather data for analysis and to communicate among physicians and public health officials [WTE01]. Some of these systems go beyond health care data to include nonhealth data such as over-the-counter (OTC) pharmaceutical sales and absenteeism that might indicate people with symptoms who have not sought health care [Hen04].There are a number of technological, logistical, and legal constraints to obtaining appropriate data and effective operation of syndromic surveillance systems [Bue04]. However, even with access to the requisite data and perfect organizational coordination and cooperation, the statistical challenges in reliably and accurately detecting a bioevent are formidable. The object of these surveillance systems, of course, is to analyze a stream of data in realtime and determine whether there is an anomaly suggesting that an incident has occurred. All data streams, however, have some degree of natural variability. These include seasonal or weekly patterns, a flu season that appears at a different time each winter or perhaps not at all, differences in coding practices, sales promotions for OTC medications, and random fluctuations due to small numbers of individuals with particular symptoms. Fur...

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Section: Applying Spc To Syndromic Surveillancementioning

confidence: 97%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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“…Each element of the score vectors (t r ) corresponds to a single batch and depicts the overall variability of this batch with respect to the other batches in the database throughout the entire batch duration (Nomikos and MacGregor, 1995). Usually, a few principal components can express most of the variability in the data when there is a high degree of correlation among the data (R << min(I, JK)).…”

Section: Multiway Principal Component Analysismentioning

confidence: 99%

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Lee

Vanrolleghem²

2003

Biotech & Bioengineering

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Abstract:Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.

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“…However, the prediction of the future behaviour of the evolving batch is necessary for the monitoring. In the literature, several methods to achieve the prediction of absent part of batches can be found [2,27,28].…”

Section: Kassidas Et Al's Approachmentioning

confidence: 99%

“…The Multivariate Statistical Process Control (SPC) monitoring scheme provides an on-line diagnostic tool to interrogate the latent model about possible reasons of faults in non-finished batches [2]. Later on, corrective actions can be applied to avoid future batches that are affected by the same faults.…”

Section: On-line Dtw Synchronization For Batch Monitoringmentioning

confidence: 99%

“…Nomikos and MacGregor [2,3], in their pioneering work, introduced Batch Multivariate Statistical Process Control (BMSPC) based on Unfold Principal Component Analysis (U-PCA) and Unfold Partial Least Squares (U-PLS). Afterwards, other multiway methods were proposed to build empirical models for its subsequent application in monitoring batch process, such as Tucker models [4] and Parallel Factor Analysis (PARAFAC) [5].…”

Section: Introductionmentioning

confidence: 99%

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Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

González-Martínez

Ferrer

Westerhuis

2011

Chemometrics and Intelligent Laboratory Systems

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This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)-or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al.'s approach [1] will be introduced to achieve the on-line synchronization of batch trajectories using the Dynamic Time Warping (DTW) algorithm. In the proposed adaptation, a new boundaries definition is presented for accurate on-line synchronization of an ongoing batch, together with a way to adapt mapping boundaries to batch length. A relaxed greedy strategy is introduced to avoid assessing the optimal path each time a new sample is available. The key advantages of the proposed strategy are its computational speed and accuracy for the batch process context. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisae cultivation are used to illustrate the performance of the proposed strategy.

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Multivariate SPC Charts for Monitoring Batch Processes

Cited by 1,155 publications

References 40 publications

Evaluating Statistical Methods for Syndromic Surveillance

Evaluating Statistical Methods for Syndromic Surveillance

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

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