STATISTICAL PROCESS CONTROL |
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| Statistical
Process Control (SPC) is widely applied in the parts manufacturing
industries and has been practised at various levels for more than 30
years. In response to current total quality initiatives, SPC has
only just recently begun to be implemented in the process
industries, where automatic feedback control systems are deeply
entrenched.
Unlike automatic control techniques, the application of SPC does not imply the implementation of a specific algorithm or its variants. IMHO, it refers to the integrated use of a collection of tools, many of which have statistical bases while some do not, to solve process problems with the view to improving product quality and process productivity at reduced costs. The key objective is to bring and keep processes in a state where any remaining variations are those inherent to the process. Current implementation strategies fall into 3 main categories: |
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Conventional SPC |
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| SPC
has been traditionally achieved by successive plotting and comparing
a statistical measure of the variable with some user defined
'control' limits. The most common charts used are the Shewhart,
Exponential Moving Average (EWMA), range and Cumulative Sum (CuSum)
charts. If the plotted statistic exceeds these limits, the process
is considered to be out of statistical control. Corrective action is
then applied in the form of identification, elimination or
compensation for the 'assignable' causes of variation, using a
variety of tools and procedures. There
is a set of notes on this site, that introduces some of the key
concepts and tools of conventional SPC (requires a browser
that is capable of displaying frames).
Multivariate statistical procedures are currently in vogue. Techniques such as Principal Components Analysis (PCA, discovered almost a 100 years ago) and Projections to Latent Structures (PLS), are being used in process fault detection and diagnosis strategies. Much work is still going on in this area. |
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Algorithmic SPC |
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| Conventional SPC, including so called Multivariate SPC (MVSPC) methods, are basically off-line techniques. They are not embued with the ability to adjust the process automatically to keep it within specifications. Whilst there are many reports of succesful cases in the parts manufacturing sector, this 'passive' control strategy does not suit continuous systems. Here, in addition to keeping products within specifications, there is a requirement to keep the process operating. Depending on the complexity of the process, the time taken to identify, eliminate and compensate for assignable causes of variation may not be acceptable. Nevertheless, the aim of both automatic process control and SPC is to increase plant profitability. Thus, it is reasonable to expect that the merger of these two apparently dichotomous methodologies could yield strategies that inherit the benefits associated with the parent approaches. This has been a subject of recent investigations [MacGregor, 1988; Tucker, 1989] where SPC is used to monitor the performances of automatic control loops. Such a strategy is sometimes called 'Algorithmic SPC' (ASPC), referring to the integrated use of algorithmic model based controllers and SPC techniques. Note, though, that the process is still being controlled by an automatic controller, that is the process is being controlled all the time. | ||
Active SPC |
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| Another way to integrate the two control approaches is to provide on-line SPC directly. Statistical models are used not only to define control limits, but also to develop control laws that suggest the degree of manipulation to maintain the process under statistical control. Thus, in applications to continuous processes, the need for an algorithmic automatic controller is avoided, leading to a direct or 'active' SPC strategy [Efthimiadu and Tham 1990, 1991; Efthimiadu et al, 1993]. Indeed, the technique is designed specifically for continuous systems. In contrast to ASPC, manipulations are made only when necessary, as indicated by detecting violation of control limits. As a result, compared to automatic control and ASPC, savings in the use of raw materials and utilities could be achieved using active SPC. | ||
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