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Conference proceeding
Statistical process control for AR(1) or non-Gaussian processes using wavelets coefficients
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
Web of Science ID: WOS:000368103000043
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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- Title
- Statistical process control for AR(1) or non-Gaussian processes using wavelets coefficients
- Publication Details
- Journal of Physical Science : Conference Series (JPCS), Vol.659, 012043
- Resource Type
- Conference proceeding
- Conference
- 12th European Workshop on Advanced Control and Diagnosis (ACD 2015) (Pilsen, Czech Republic, 11/19/2015–11/20/2015)
- Publisher
- Institute of Physics Publishing Ltd.; United Kingdom
- Series
- 659
- Copyright
- Published under licence by IOP Publishing Ltd Permission granted to the University of West Florida Libraries to digitize and/or display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
- Identifiers
- WOS:000368103000043; 99380090779606600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English