Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data
| Autor: | Rosemeire L. Fiaccone, Christopher Ulloa-Correa, Diego C. Nascimento, Paulo H. Ferreira, Francisco Louzada, Anderson Fonseca, Ayón García-Piña |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Logic
Monte Carlo method 02 engineering and technology 01 natural sciences Standard deviation 010104 statistics & probability Chart Law of large numbers Statistics 0202 electrical engineering electronic engineering information engineering QA1-939 Control chart 0101 mathematics Mathematical Physics Monte Carlo simulation Mathematics Algebra and Number Theory relative air humidity monitoring MÉTODO DE MONTE CARLO Statistical process control Symbolic Data Analysis (SDA) in Statistical Process Control (SPC) Control limits unit-Lindley distribution 020201 artificial intelligence & image processing rates and proportions data Geometry and Topology Bernoulli process Analysis |
| Zdroj: | Axioms, Vol 10, Iss 154, p 154 (2021) Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Axioms Volume 10 Issue 3 |
| ISSN: | 2075-1680 |
| Popis: | Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework. |
| Databáze: | OpenAIRE |
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