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Statistical process control for AR(1) or non-Gaussian processes using wavelets coefficients
Conference proceeding   Open access

Statistical process control for AR(1) or non-Gaussian processes using wavelets coefficients

Achraf Cohen, Teodor Tiplica and Abdessamad Kobi
Journal of Physical Science : Conference Series (JPCS), Vol.659, 012043
659
12th European Workshop on Advanced Control and Diagnosis (ACD 2015) (Pilsen, Czech Republic, 11/19/2015–11/20/2015)
2015
DOI: https://doi.org/10.1088/1742-6596/659/1/012043
Web of Science ID: WOS:000368103000043

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Abstract

Autocorrelation and non-normality of process characteristic variables are two main difficulties that industrial engineers must face when they should implement control charting techniques. This paper presents new issues regarding the probability distribution of wavelets coefficients. Firstly, we highlight that wavelets coefficients have capacities to strongly decrease autocorrelation degree of original data and are normally-like distributed, especially in the case of Haar wavelet. We used AR(1) model with positive autoregressive parameters to simulate autocorrelated data. Illustrative examples are presented to show wavelets coefficients properties. Secondly, the distributional parameters of wavelets coefficients are derived, it shows that wavelets coefficients reflect an interesting statistical properties for SPC purposes.
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Published (Version of record)Conference paper pdfAttribution 3.0 Unported ,  Open Access

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