Control charts for non-Gaussian distributions

Florina Babus; Abdessamad Kobi; Th. Tiplica; Ioan Bacivarov; Angelica Bacivarov

doi:10.1117/12.741893

31 May 2007 Control charts for non-Gaussian distributions

Florina Babus, Abdessamad Kobi, Th. Tiplica, Ioan Bacivarov, Angelica Bacivarov

Proceedings Volume 6635, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III; 66350I (2007) https://doi.org/10.1117/12.741893
Event: Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III, 2006, Bucharest, Romania

Abstract

Traditional statistical process control (SPC) techniques applied in the industrial processes field consider often that the distribution ofdata is Gaussian. The estimation ofparameters, the detection ofthe out oforder situations and the control of the followed characteristics are easy to achieve for the normal populations. In reality, whatever the origin of a characteristic (large series productions for components, mechanical parts of OE communication systems, etc. ) the curve of distributions of the measured values is generally far from being normal. The simple approximation to the Gauss distribution and the use of the classical control methods sometimes induces serious errors. In this paper, a study on the statistical control of non Gaussian populations is presented. Particularly we discuss the Rayleigh and the Weibull distribution as being representatives in (SPC for some category of data. The X control charts with variable limits are tested. Experimental simulations are presented for different parameters of the two distributions. The results confirm the methodology and encourage the research in the field of non Gaussian processes.

Citation Download Citation

Florina Babus, Abdessamad Kobi, Th. Tiplica, Ioan Bacivarov, and Angelica Bacivarov "Control charts for non-Gaussian distributions", Proc. SPIE 6635, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III, 66350I (31 May 2007); https://doi.org/10.1117/12.741893

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