Economic Control of Quality of Manufactured Product.

The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.

https://www.jstor.org/stable/pdfplus/1269152.pdf

Multivariate SPC charts for monitoring batch processes

A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.

A multivariate exponentially weighted moving average control chart

http://automatica.dei.unipd.it/public/Schenato/PSC/2010_2011/gruppo4-Building_termo_identification/IdentificazioneTermodinamica20072008/Biblio/Articoli/Multi-way%20partial%20least%20squares%20in%20monitoring%20batch%20processes.pdf

Multi-way partial least squares in monitoring batch processes

A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s. Multivariate cumulative sum (CUSUM) control procedures and a multivariate exponentially weighted moving average (EWMA) control chart are reviewed and recommendations are made regarding their use. Several recent articles that give methods for interpreting an out-of-control signal on a multivariate control chart are analyzed and discussed. Other topics such as the use of principal components and regression adjustment of variables in MQC, as well as frequently used approximations in MQC, are discussed.

A review of multivariate control charts

Roberts (1959) first introduced the exponentially weighted moving average (EWMA) control scheme. Using simulation to evaluate its properties, he showed that the EWMA is useful for detecting small shifts in the mean of a process. The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done. In this article, we evaluate the properties of an EWMA control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value. A design procedure for EWMA control schemes is given. Parameter values not commonly used in the literature are shown to be useful for detecting small shifts in a process. In addition, several enhancements to EWMA control schemes are considered. These include a fast initial response feature that makes the EWMA control scheme more sensitive to start-up problems, a combined Shewhart EWMA that provides protection against both larg...

https://www.jstor.org/stable/pdfplus/1269835.pdf

Exponentially weighted moving average control schemes: properties and enhancements

The goal of algorithmic statistical process control is to reduce predictable quality variations using feedback and feedforward techniques and then monitor the complete system to detect and remove unexpected root causes of variation. This methodology seeks to exploit the strengths of both automatic control and statistical process control (SPC), two fields that have developed in relative isolation from one another. Recent experience with the control and monitoring of intrinsic viscosity from a particular General Electric polymerization process has led to a better understanding of how SPC and feedback control can be united into a single system. Building on past work by MacGregor, Box, Astrom, and others, the article covers the application from statistical identification and modeling to implementing feedback control and final SPC monitoring. Operational and technical issues that arose are examined, and a general approach is outlined.

Algorithmic statistical process control: concepts and an application

A system and method for automatically predicting timing and costs of future service events in a life cycle of a product. A database contains a plurality of service information and performance information for the product. A statistical analyzer automatically analyzes the plurality of processed service information to determine a plurality of compartment failure information. A performance deterioration rate analyzer automatically analyzes the performance deterioration rate of the product from the plurality of service information and performance information. A simulator, automatically simulates a distribution of future service events in the life cycle of the product and predicts the costs of the service events according to the plurality of compartment failure information and the performance deterioration rate analysis.

System and method for automatically predicting the timing and costs of service events in a life cycle of a product

There are many instances in which the quality of a product or constancy of a process is determined by the joint levels of several attributes or properties. During the conduct of such a process or the production of such a product, one wishes to detect as quickly as possible any departure from a satisfactory state, while at the same time identifying which attributes are responsible for the deviation. In most cases of practical interest, however, there exist correlations among the several properties of interest; this makes it advisable to monitor certain aggregate characteristics of the process, rather than observing its various components separately. When the mean vector of the quality attributes is the major concern, this aggregate monitoring function is most commonly implemented via a T 2 chart. The dependencies among attributes, however, complicate the determination of which are responsible when a deviation occurs. This paper presents an approach to help identify aberrant variables when Shewhart type mul...

Identification of out of control quality characteristics in a multivariate manufacturing environment

Statistical process control (SPC) has traditionally been applied to processes in which successive observations would ideally be independent and identically distributed as a basis for achieving fundamental process improvement. Stochastic control, on the other hand, addresses situations in which observations are dynamically related over time; its intent is to run the existing process well, as opposed to improving it as such. A schema is presented for uniting traditional SPC and feedforward/feedback control into a system that exploits the strengths of both, Building on past work by MacGregor, Box, Astrom, and others, we discuss the theory and practice of such an approach, along with a consideration of research and technical issues that arise.

William T. Tucker

Papers

Exponentially weighted moving average control schemes: properties and enhancements

Algorithmic statistical process control: concepts and an application

System and method for automatically predicting the timing and costs of service events in a life cycle of a product

Identification of out of control quality characteristics in a multivariate manufacturing environment

Algorithmic Statistical Process Control: An Elaboration