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PL
Klasyczne metody statystycznego sterowania procesem wykorzystują założenie o rozkładzie normalnym badanej cechy. W sytuacji, gdy warunek ten nie jest spełniony, wykorzystuje się odpowiednie transformacje lub korzysta się ze specyficznych, odpornych na rodzaj rozkładu metod. W pracy przedstawiona zostanie próba wykorzystania sztucznych sieci neuronowych do konstrukcji wielowymiarowych kart kontrolnych. Przeprowadzone zostaną symulacje dla rozkładu normalnego i chi-kwadrat.
EN
In classical statistical process control the assumption of normal distribution is usually valid. When this condition is not satisfied specifically transformation or the specified method are used. This article presents application of artificial neural networks to construct of multivariate control charts for individual data.
PL
Klasyczne karty kontrolne wykorzystują sekwencje parametrycznych te-stów statystycznych. Zwykle wymagają spełnienia założeń dotyczących postaci rozkładu. W przypadku, gdy założenia takie nie są spełnione, nie jest uzasadnione ich stosowanie. W artykule przedstawiono propozycję wykorzystania karty kontrolnej opartej na sekwencji testów permutacyjnych. Testy permutacyjne nie wymagają spełnienia założenia o postaci rozkładu porównywanych zmiennych. Własności proponowanej karty zostały porównane z własnościami klasycznych kart kontrolnych z wykorzystaniem symulacji komputerowych. W symulacjach wykorzystano wartości losowe generowane z uogólnionego rozkładu lambda.
EN
The control charts are used for monitoring technological processes. These tools are a graphical view of the sequence of parametric tests. The main assumption is that the process data are normally and independently distributed with mean μ and standard deviation σ. The control chart can’t be used when the random variables are not normally distributed. There are some methods for monitoring non-normal processes. The proposal of the permutation tests use instead of the parametric tests in monitoring pro-cesses is presented in the paper. The results of Monte Carlo study for classical control charts and control charts based on the permutation tests are presented in the paper.
EN
In the statistical process control the assumption of normal distribution is widely used. But in practical this assumption is often unfulfilled. In this situation appropriate transformation or nonclassical method should be used. The most commonly used methods of transformation are the curves Johnson. The most important problem with the use of Johnson curves is estimation of parameter and form of curves. This paper presents improvement of Slifker and Shapiro procedure (1980) by construction of optimization model based on chi-square statistic.
EN
The control chart is a tool of statistical quality control, which is widely used in factories. The fulfillment of its basic assumptions ensures faultless assessing the monitored process. Infringements the assumptions of classical control charts can cause false signals in the case of a regulated process, either lack of signal or the signal delayed in time, when process is out-of-control. Incorrect assessment of the accuracy of the manufacturing process is of course the economic impact. In this paper, based on actual data an attempt to determine control limits for the manufacturing process of the distribution of the controlled characteristics, which is significantly different from a normal distribution, was taken. The result of this work is the method of determining the control limits based on the quantile of a random variable estimated by the kernel estimation. The article pays attention to the economic consequences of infringements the assumptions of classical control charts.
EN
One of the most important tools of statistical quality control is a process capability analysis. In order to measure process capability the most used are indices constructed for one-dimensional characteristic of production process. However often production process is described by more than one characteristic. One should then conduct a multidimensional assessment of process capability with appropriately designed indicators. The aim of this article is to analyze the problem of process capability measure of multi-dimensional process with dependent characteristics, using a proposed multidimensional process capability index.
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